A review of object detection based on deep learning

With the rapid development of deep learning techniques, deep convolutional neural networks (DCNNs) have become more important for object detection. Compared with traditional handcrafted feature-based methods, the deep learning-based object detection methods can learn both low-level and high-level im...

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Published inMultimedia tools and applications Vol. 79; no. 33-34; pp. 23729 - 23791
Main Authors Xiao, Youzi, Tian, Zhiqiang, Yu, Jiachen, Zhang, Yinshu, Liu, Shuai, Du, Shaoyi, Lan, Xuguang
Format Journal Article
LanguageEnglish
Published New York Springer US 01.09.2020
Springer Nature B.V
Subjects
Algorithms
Artificial neural networks
Computer Communication Networks
Computer networks
Computer Science
Data Structures and Information Theory
Deep learning
Machine learning
Meteorological satellites
Multimedia Information Systems
Neural networks
Object recognition
Special Purpose and Application-Based Systems
Deep learning
Deep convolutional neural networks
Computer vision
Object detection
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Abstract With the rapid development of deep learning techniques, deep convolutional neural networks (DCNNs) have become more important for object detection. Compared with traditional handcrafted feature-based methods, the deep learning-based object detection methods can learn both low-level and high-level image features. The image features learned through deep learning techniques are more representative than the handcrafted features. Therefore, this review paper focuses on the object detection algorithms based on deep convolutional neural networks, while the traditional object detection algorithms will be simply introduced as well. Through the review and analysis of deep learning-based object detection techniques in recent years, this work includes the following parts: backbone networks, loss functions and training strategies, classical object detection architectures, complex problems, datasets and evaluation metrics, applications and future development directions. We hope this review paper will be helpful for researchers in the field of object detection.
AbstractList With the rapid development of deep learning techniques, deep convolutional neural networks (DCNNs) have become more important for object detection. Compared with traditional handcrafted feature-based methods, the deep learning-based object detection methods can learn both low-level and high-level image features. The image features learned through deep learning techniques are more representative than the handcrafted features. Therefore, this review paper focuses on the object detection algorithms based on deep convolutional neural networks, while the traditional object detection algorithms will be simply introduced as well. Through the review and analysis of deep learning-based object detection techniques in recent years, this work includes the following parts: backbone networks, loss functions and training strategies, classical object detection architectures, complex problems, datasets and evaluation metrics, applications and future development directions. We hope this review paper will be helpful for researchers in the field of object detection.
Author Tian, Zhiqiang
Zhang, Yinshu
Liu, Shuai
Du, Shaoyi
Yu, Jiachen
Lan, Xuguang
Xiao, Youzi
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  surname: Lan
  fullname: Lan, Xuguang
  organization: Institute of Artificial Intelligence and Robotics, Xi’an Jiaotong University
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Cites_doi 10.1038/nature14539
10.1109/CVPR.2018.00062
10.1109/ICASSP.2013.6638947
10.1109/CVPR.2017.324
10.1109/34.730558
10.1109/ICCV.2017.440
10.1109/CVPR.2017.474
10.1109/ICCV.2009.5459207
10.21437/Interspeech.2013-552
10.1109/TMM.2016.2642789
10.1109/CVPR.2019.00094
10.1007/978-3-319-10593-2_33
10.1007/978-3-319-10602-1_47
10.1109/BTAS.2017.8272675
10.1109/ICME.2008.4607625
10.1109/CVPR.2016.521
10.1109/CVPR.2018.00745
10.1016/j.media.2017.07.005
10.1145/3065386
10.1023/B:VISI.0000029664.99615.94
10.1109/ICRA.2011.5980382
10.1007/978-3-030-01267-0_28
10.1109/TPAMI.2012.28
10.1109/ICCV.2009.5459257
10.1109/ICCV.2017.593
10.1109/ICCV.2015.221
10.1109/ICCV.2017.212
10.1109/CVPR.2015.7298965
10.1109/CVPR.2016.95
10.1109/CVPR.2016.451
10.1109/CVPR.2014.223
10.1109/TETC.2016.2593643
10.3354/cr030079
10.1109/ICCV.2019.00970
10.1007/978-3-319-10602-1_26
10.1109/CVPR.2018.00377
10.5194/gmd-7-1247-2014
10.1109/TMM.2007.893351
10.1109/ICCV.2015.296
10.1109/CVPR.2017.643
10.21629/JSEE.2018.05.07
10.1109/CVPR.2015.7298731
10.1007/978-3-030-01264-9_9
10.1109/ICME.2019.00009
10.1007/978-3-319-10590-1_53
10.1109/CVPR.2010.5539957
10.1109/CVPR.2018.00442
10.1109/ICCV.2019.00667
10.1109/CVPR.2018.00724
10.1109/TPAMI.2016.2601099
10.1007/978-3-030-20887-5_34
10.1109/ICCV.2009.5459183
10.1007/s10479-005-5724-z
10.1007/978-3-319-46493-0_32
10.1007/978-3-319-16811-1_34
10.1109/CVPR.2019.00293
10.1016/j.cirp.2016.04.072
10.1117/12.2304811
10.1109/LSP.2016.2603342
10.1109/CVPR.2017.441
10.1007/978-3-030-01264-9_8
10.1007/978-3-642-15555-0_42
10.1109/CVPR.2017.351
10.1162/neco.1997.9.8.1735
10.1007/978-3-319-10602-1_48
10.5244/C.30.15
10.1109/CVPR.2016.343
10.1109/TGRS.2017.2778300
10.1093/ijl/3.4.235
10.1109/CVPR.2017.557
10.1109/CVPR.2019.00720
10.1109/CVPR.2018.00647
10.1109/ICCV.2007.4408986
10.1038/323533a0
10.1109/CVPR.2006.119
10.1109/CVPR.2017.690
10.1109/CVPR.2017.65
10.1109/ICCV.2019.00972
10.1109/CVPR.2014.49
10.1109/TPAMI.2009.167
10.1109/CVPR.2018.00474
10.1007/s11263-009-0275-4
10.1109/ICCV.2017.368
10.1109/IROS.2012.6385773
10.1007/978-3-319-46478-7_22
10.1109/CVPR.2016.350
10.1609/aaai.v33i01.33018094
10.1007/978-3-030-01252-6_24
10.1109/CVPR.2016.98
10.1007/978-3-319-49409-8_69
10.1007/978-3-319-15554-8_73
10.1109/CVPR.2018.00753
10.1007/978-3-319-47157-0_20
10.1109/CVPR.2016.314
10.1109/CVPR.2017.75
10.1007/978-3-319-10584-0_23
10.1109/CVPR.2019.00091
10.1109/CVPR.2018.00914
10.1007/978-0-387-84858-7_14
10.1109/CVPR.2017.639
10.1109/CVPR.2018.00907
10.1109/CVPR.2018.00633
10.3390/app8050813
10.1109/5.726791
10.5244/C.28.82
10.1007/978-3-030-01219-9_39
10.1016/j.cviu.2005.09.012
10.1007/978-3-030-01258-8_15
10.1007/978-3-319-23528-8_9
10.1007/978-3-319-46493-0_22
10.1007/978-3-319-48881-3_56
10.1109/CVPR.2015.7299170
10.1109/ICIP.2002.1038171
10.1016/j.patcog.2016.04.002
10.1145/2847263.2847265
10.1109/CVPR.2015.7298938
10.1609/aaai.v31i1.11231
10.1109/TPAMI.2013.50
10.1007/s11263-014-0733-5
10.1109/ICCV.2019.00615
10.1109/CVPRW.2009.5206532
10.1109/ICMEW.2015.7169816
10.1007/s11263-013-0620-5
10.1109/ICPR.2006.479
10.1109/CVPR.2018.00811
10.1109/CVPR.2016.618
10.1109/CVPR.2019.00958
10.1109/ICCV.2015.312
10.1109/CVPRW.2018.00215
10.1109/TPAMI.2016.2578328
10.1109/ICCV.2019.00502
10.1109/CVPR.2014.222
10.1109/TPAMI.2017.2738644
10.1109/CVPR.2015.7298958
10.1109/CVPR.2015.7298854
10.1109/ICCV.2017.52
10.1007/978-3-319-46726-9_31
10.1109/TPAMI.2017.2723009
10.1007/978-3-319-46448-0_2
10.1109/CVPR.2016.89
10.1109/CVPR.2009.5206848
10.1109/ICCV.2017.322
10.1109/ICCV.2019.00671
10.1109/CVPR.2013.417
10.1109/IVS.2016.7535375
10.1109/CVPR.2017.106
10.1109/CVPR.2008.4587597
10.1109/TPAMI.2011.155
10.1109/ICCV.2015.285
10.1109/TPAMI.2010.70
10.1109/CVPR.2017.195
10.1109/ICCV.2011.6126474
10.1109/CVPR.2017.691
10.1109/CVPR.2016.91
10.1007/978-3-030-01264-9_45
10.1109/FG.2017.82
10.1023/A:1007379606734
10.1109/IVS.2018.8500504
10.1109/TPAMI.2011.231
10.1109/TPAMI.2006.79
10.1109/CVPR.2018.00609
10.1109/CVPR.2001.990517
10.1109/ICCV.2015.169
10.1109/CVPR.2016.90
10.1007/978-3-030-01240-3_21
10.1109/ICCV.2017.522
10.1109/ICCV.2003.1238663
10.1109/CVPR.2018.00941
10.1109/ICCV.2011.6126456
10.1109/CVPR.2014.81
10.1109/CVPR.2014.220
10.1109/TPAMI.2008.128
10.1109/ICCV.2017.54
10.1109/TCSVT.2017.2736553
10.1109/CVPR.2018.00644
10.1007/978-3-319-10578-9_23
10.1109/CVPR.2018.00730
10.1609/aaai.v33i01.33019259
10.1109/CVPR.2019.01082
10.1007/978-3-319-46493-0_50
10.1109/CVPR.2017.207
10.1109/TPAMI.2014.2343217
10.1109/ICCV.2017.30
10.1109/CVPR.2017.211
10.1142/9789812797926_0003
10.1109/ICCV.2013.257
10.1007/s11263-015-0816-y
10.1109/LGRS.2018.2872355
10.1109/ICCV.2017.444
10.1109/CVPR.2006.68
10.1109/CVPR.2014.276
10.1109/CVPR.2015.7298594
10.1109/TGRS.2016.2601622
10.1109/CVPR.2017.553
10.4467/20838476SI.16.004.6185
10.1109/ICCV.2019.00975
10.1007/978-3-030-01264-9_48
10.1007/s11263-014-0748-y
10.1109/ICCV.2017.324
10.1007/978-3-319-46475-6_28
10.1007/978-3-319-46723-8_28
10.1109/TGRS.2016.2645610
10.1609/aaai.v32i1.11716
10.1109/CVPR.2016.308
10.1109/CVPR.2017.243
10.1007/978-3-030-11009-3_11
10.1109/CVPR.2019.00300
10.1109/CVPR.2005.177
10.1109/CVPR.2017.634
10.1109/ACPR.2013.33
10.1109/TGRS.2019.2899955
10.1109/CVPR.2017.166
10.1109/CVPR.2015.7299188
10.1007/s11263-019-01247-4
10.1007/978-3-030-01261-8_33
10.1109/CVPRW.2009.5206631
10.1007/s11263-015-0822-0
10.1007/978-3-030-01246-5_2
10.1109/CVPR.2019.00237
10.3390/rs11060620
10.1007/978-3-030-01228-1_15
10.1023/B:VISI.0000013087.49260.fb
10.1109/CVPR.2018.00136
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Computer vision
Object detection
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References CR162
CR283
CR163
CR284
CR160
CR281
CR282
CR280
Kang, Li, Yan, Zeng, Yang, Xiao, Zhang, Wang, Wang, Wang (CR102) 2018; 28
CR159
CR157
CR278
CR279
CR276
CR156
CR277
CR274
CR154
CR275
Bengio, Courville, Vincent (CR8) 2013; 35
Fei-Fei, Fergus, Perona (CR56) 2007; 106
CR173
CR294
CR174
CR295
CR171
CR292
CR172
CR293
CR290
CR170
CR291
CR168
CR289
CR169
Yang, Luo, Loy, Tang (CR257) 2017; 40
CR166
CR287
CR167
CR288
CR164
CR285
CR165
Li, Wei, Liang, Dong, Xu, Feng, Yan (CR129) 2017; 19
CR140
CR261
CR141
CR262
CR260
CR137
CR258
CR138
CR259
CR135
CR256
CR136
CR133
CR254
CR134
CR255
CR131
CR252
CR132
CR253
CR151
CR272
CR152
CR270
CR150
CR271
Zhang, Zhang, Li, Qiao (CR273) 2016; 23
CR148
CR269
CR146
CR267
CR147
CR268
CR144
CR265
CR266
CR142
CR263
CR143
Mao, Yao, Tang, Li, Yao, Wang (CR158) 2018; 6
Xiao, Ehinger, Hays, Torralba, Oliva (CR251) 2016; 119
Hochreiter, Schmidhuber (CR86) 1997; 9
CR240
CR115
CR236
CR237
CR113
CR114
CR235
CR111
Hoi, Wu, Liu, Wu, Wang, Xue, Wu (CR87) 2015; 46
CR112
CR233
CR110
CR231
CR119
Miller, Beckwith, Fellbaum, Gross, Miller (CR161) 1990; 3
CR117
CR238
CR118
CR239
CR250
Rumelhart, Hinton, Williams (CR194) 1986; 323
Li, Cheng, Bu, You (CR130) 2017; 56
CR126
CR248
CR124
Russakovsky, Deng, Su, Krause, Satheesh, Ma, Huang, Karpathy, Khosla, Bernstein (CR195) 2015; 115
CR125
CR246
CR243
CR123
CR120
CR241
CR121
CR242
CR128
CR249
Bartlett, Wegkamp (CR6) 2008; 9
Ren, He, Girshick, Zhang, Sun (CR190) 2016; 39
Fei-Fei, Fergus, Perona (CR55) 2006; 28
CR19
CR18
CR17
CR16
CR15
CR14
CR13
CR12
CR11
CR214
CR10
CR215
CR212
CR213
CR210
CR211
Caruana (CR20) 1997; 28
CR218
Liangkui, Shaoyou, Zhongxing (CR139) 2018; 29
CR219
CR216
CR217
CR29
CR27
CR26
CR25
CR24
CR23
CR104
CR225
CR21
CR105
CR226
CR223
CR103
CR100
CR221
CR101
Yu, Wong (CR264) 2007; 9
CR220
Felzenszwalb, Girshick, McAllester, Ramanan (CR58) 2010; 32
CR108
CR229
CR109
CR106
CR227
CR107
CR228
Litjens, Kooi, Bejnordi, Setio, Ciompi, Ghafoorian, Van Der Laak, Van Ginneken, Sanchez (CR145) 2017; 42
CR39
CR38
CR37
CR35
He, Lau, Liu, Huang, Yang (CR83) 2015; 115
CR34
CR32
Everingham, Van Gool, Williams, Winn, Zisserman (CR52) 2010; 88
CR31
Srivastava, Hinton, Krizhevsky, Sutskever, Salakhutdinov (CR209) 2014; 15
CR30
De Boer, Kroese, Mannor, Rubinstein (CR41) 2005; 134
Torralba, Fergus, Freeman (CR222) 2008; 30
Lowe (CR155) 2004; 60
Willmott, Matsuura (CR247) 2005; 30
CR49
CR48
CR47
CR45
CR44
CR203
CR43
CR204
CR42
CR201
CR202
CR40
CR200
Wei, You, Li (CR244) 2016; 58
CR207
CR208
CR205
CR206
Wang, Zheng, Lei, Bai (CR234) 2019; 11
Viola, Jones (CR230) 2004; 57
Weimer, Scholz-Reiter, Shpitalni (CR245) 2016; 65
CR59
CR57
CR54
CR53
CR50
Uijlings, van de Sande, Gevers, Smeulders (CR224) 2013; 104
CR69
CR68
CR67
CR66
CR65
CR64
CR63
CR62
CR61
CR60
Chai, Draxler (CR22) 2014; 7
CR79
CR78
CR77
Alexe, Deselaers, Ferrari (CR1) 2012; 34
CR76
CR75
CR74
CR73
CR199
CR72
CR71
CR197
CR70
CR198
Zhou, Lapedriza, Khosla, Oliva, Torralba (CR286) 2018; 40
Cheng, Zhou, Han (CR33) 2016; 54
Li, Liang, Shen, Xu, Feng, Yan (CR127) 2017; 20
CR2
CR4
CR3
CR5
CR7
CR9
CR89
CR88
CR85
CR84
CR82
CR81
CR80
Everingham, Eslami, Van Gool, Williams, Winn, Zisserman (CR51) 2015; 111
CR184
CR185
CR182
CR183
CR180
CR181
CR99
CR98
CR97
CR179
CR96
CR95
CR177
CR94
CR178
CR93
CR175
CR296
CR92
CR176
CR297
CR91
CR90
Krizhevsky, Sutskever, Hinton (CR116) 2017; 60
Ren, Zhu, Xiao (CR193) 2018; 8
Long, Gong, Xiao, Liu (CR153) 2017; 55
Dollar, Wojek, Schiele, Perona (CR46) 2012; 34
CR196
Chen, Song, Dong, Huang, Hua, Yan (CR28) 2015; 37
LeCun, Bengio, Hinton (CR122) 2015; 521
CR191
CR192
Liu, Yuan, Sun, Wang, Zheng, Tang, Shum (CR149) 2010; 33
CR188
CR189
Wang, Bai, Wang, Zhou, Ren (CR232) 2018; 16
CR186
CR187
Cortes, Vapnik (CR36) 1995; 20
8976_CR91
8976_CR92
8976_CR90
8976_CR95
8976_CR96
8976_CR93
8976_CR94
8976_CR99
8976_CR97
8976_CR98
T Chai (8976_CR22) 2014; 7
Y LeCun (8976_CR122) 2015; 521
P Dollar (8976_CR46) 2012; 34
GA Miller (8976_CR161) 1990; 3
P-T De Boer (8976_CR41) 2005; 134
G Cheng (8976_CR33) 2016; 54
8976_CR210
8976_CR212
8976_CR211
8976_CR214
8976_CR213
8976_CR216
8976_CR215
8976_CR218
J Wang (8976_CR234) 2019; 11
8976_CR217
8976_CR219
Y Long (8976_CR153) 2017; 55
8976_CR201
8976_CR200
8976_CR203
8976_CR202
8976_CR205
8976_CR204
8976_CR207
8976_CR206
K Kang (8976_CR102) 2018; 28
8976_CR208
O Russakovsky (8976_CR195) 2015; 115
D Weimer (8976_CR245) 2016; 65
Y Ren (8976_CR193) 2018; 8
8976_CR111
8976_CR110
8976_CR231
8976_CR113
8976_CR112
8976_CR233
8976_CR115
8976_CR236
8976_CR114
8976_CR235
8976_CR117
8976_CR238
T Liu (8976_CR149) 2010; 33
8976_CR237
8976_CR119
8976_CR118
8976_CR239
PL Bartlett (8976_CR6) 2008; 9
J Li (8976_CR129) 2017; 19
S Yang (8976_CR257) 2017; 40
8976_CR100
8976_CR221
C Wang (8976_CR232) 2018; 16
8976_CR220
8976_CR223
8976_CR101
8976_CR104
8976_CR225
8976_CR103
8976_CR106
8976_CR227
8976_CR105
8976_CR226
8976_CR108
8976_CR229
8976_CR107
8976_CR228
8976_CR109
8976_CR2
8976_CR15
C Cortes (8976_CR36) 1995; 20
8976_CR3
8976_CR16
8976_CR4
8976_CR13
L Liangkui (8976_CR139) 2018; 29
8976_CR5
8976_CR14
8976_CR19
8976_CR17
8976_CR18
8976_CR7
8976_CR140
8976_CR261
8976_CR9
8976_CR260
8976_CR131
8976_CR252
8976_CR133
8976_CR254
8976_CR132
H Mao (8976_CR158) 2018; 6
8976_CR253
8976_CR135
8976_CR256
L Fei-Fei (8976_CR55) 2006; 28
8976_CR134
8976_CR255
K Li (8976_CR130) 2017; 56
8976_CR137
8976_CR258
8976_CR136
8976_CR138
8976_CR259
8976_CR11
8976_CR12
8976_CR10
8976_CR26
8976_CR27
8976_CR24
8976_CR25
PF Felzenszwalb (8976_CR58) 2010; 32
8976_CR29
8976_CR250
8976_CR120
8976_CR241
8976_CR240
8976_CR243
8976_CR121
8976_CR242
8976_CR124
8976_CR123
8976_CR126
8976_CR125
8976_CR246
8976_CR128
8976_CR249
8976_CR248
8976_CR23
8976_CR21
8976_CR37
8976_CR38
8976_CR35
8976_CR39
B Zhou (8976_CR286) 2018; 40
8976_CR160
8976_CR281
8976_CR280
8976_CR162
8976_CR283
8976_CR282
8976_CR274
8976_CR152
8976_CR276
8976_CR154
8976_CR275
8976_CR157
8976_CR278
M Everingham (8976_CR52) 2010; 88
8976_CR156
8976_CR277
8976_CR159
8976_CR279
8976_CR30
M Everingham (8976_CR51) 2015; 111
8976_CR34
8976_CR31
8976_CR32
8976_CR48
A Torralba (8976_CR222) 2008; 30
8976_CR49
8976_CR47
8976_CR270
G Litjens (8976_CR145) 2017; 42
R Caruana (8976_CR20) 1997; 28
8976_CR151
8976_CR272
8976_CR150
8976_CR271
Y Bengio (8976_CR8) 2013; 35
8976_CR142
8976_CR263
8976_CR141
8976_CR262
8976_CR144
8976_CR265
8976_CR143
8976_CR146
8976_CR267
JRR Uijlings (8976_CR224) 2013; 104
8976_CR266
8976_CR148
8976_CR269
8976_CR147
8976_CR268
8976_CR40
8976_CR44
8976_CR45
8976_CR42
8976_CR43
8976_CR59
8976_CR57
8976_CR180
8976_CR182
8976_CR181
8976_CR184
DE Rumelhart (8976_CR194) 1986; 323
8976_CR183
8976_CR175
8976_CR296
8976_CR174
8976_CR295
8976_CR177
8976_CR176
8976_CR297
8976_CR179
8976_CR178
CJ Willmott (8976_CR247) 2005; 30
Y Wei (8976_CR244) 2016; 58
8976_CR50
J Xiao (8976_CR251) 2016; 119
S He (8976_CR83) 2015; 115
Q Chen (8976_CR28) 2015; 37
8976_CR53
8976_CR54
8976_CR68
8976_CR69
8976_CR290
DG Lowe (8976_CR155) 2004; 60
8976_CR171
8976_CR292
8976_CR170
8976_CR291
8976_CR173
8976_CR294
8976_CR172
8976_CR293
8976_CR164
8976_CR285
B Alexe (8976_CR1) 2012; 34
8976_CR163
8976_CR284
8976_CR166
8976_CR287
8976_CR165
8976_CR168
8976_CR289
8976_CR167
8976_CR288
8976_CR169
8976_CR62
8976_CR63
8976_CR60
8976_CR61
8976_CR66
8976_CR67
8976_CR64
8976_CR65
8976_CR79
J Li (8976_CR127) 2017; 20
P Viola (8976_CR230) 2004; 57
S Hochreiter (8976_CR86) 1997; 9
S Ren (8976_CR190) 2016; 39
A Krizhevsky (8976_CR116) 2017; 60
8976_CR197
8976_CR196
8976_CR199
8976_CR198
8976_CR70
Z Yu (8976_CR264) 2007; 9
8976_CR73
8976_CR74
8976_CR71
8976_CR72
8976_CR77
8976_CR78
8976_CR75
8976_CR76
SC Hoi (8976_CR87) 2015; 46
N Srivastava (8976_CR209) 2014; 15
L Fei-Fei (8976_CR56) 2007; 106
8976_CR191
K Zhang (8976_CR273) 2016; 23
8976_CR192
8976_CR186
8976_CR185
8976_CR188
8976_CR187
8976_CR80
8976_CR81
8976_CR189
8976_CR84
8976_CR85
8976_CR82
8976_CR88
8976_CR89
References_xml – ident: CR45
– ident: CR150
– ident: CR282
– ident: CR173
– ident: CR97
– ident: CR196
– ident: CR68
– ident: CR74
– ident: CR39
– ident: CR16
– ident: CR115
– ident: CR138
– ident: CR80
– ident: CR201
– ident: CR121
– ident: CR253
– ident: CR167
– ident: CR144
– ident: CR276
– ident: CR235
– ident: CR92
– ident: CR132
– ident: CR287
– ident: CR178
– ident: CR191
– ident: CR241
– ident: CR229
– ident: CR11
– ident: CR57
– ident: CR294
– ident: CR185
– ident: CR218
– ident: CR85
– ident: CR242
– ident: CR5
– ident: CR109
– volume: 323
  start-page: 533
  issue: 6088
  year: 1986
  end-page: 536
  ident: CR194
  article-title: Learning representations by back-propagating errors
  publication-title: Nature
– ident: CR126
– volume: 16
  start-page: 310
  issue: 2
  year: 2018
  end-page: 314
  ident: CR232
  article-title: Multiscale visual attention networks for object detection in VHR remote sensing images
  publication-title: IEEE Geosci Remote Sens Lett
– volume: 9
  start-page: 1823
  issue: 8
  year: 2008
  end-page: 1840
  ident: CR6
  article-title: Classification with a reject option using a hinge loss
  publication-title: J Mach Learn Res
– volume: 8
  start-page: 813
  issue: 5
  year: 2018
  ident: CR193
  article-title: Small object detection in optical remote sensing images via modified faster R-CNN
  publication-title: Appl Sci
– ident: CR207
– ident: CR91
– ident: CR213
– ident: CR156
– ident: CR179
– volume: 60
  start-page: 91
  issue: 2
  year: 2004
  end-page: 110
  ident: CR155
  article-title: Distinctive image features from scale-invariant keypoints
  publication-title: Int J Comput Vis
– ident: CR10
– ident: CR137
– ident: CR269
– ident: CR270
– ident: CR184
– volume: 55
  start-page: 2486
  issue: 5
  year: 2017
  end-page: 2498
  ident: CR153
  article-title: Accurate object localization in remote sensing images based on convolutional neural networks
  publication-title: IEEE Trans Geosci Remote Sens
– ident: CR63
– volume: 65
  start-page: 417
  issue: 1
  year: 2016
  end-page: 420
  ident: CR245
  article-title: Design of deep convolutional neural network architectures for automated feature extraction in industrial inspection
  publication-title: CIRP Annals-Manufacturing Technology
– ident: CR27
– ident: CR236
– ident: CR108
– ident: CR69
– ident: CR281
– ident: CR44
– volume: 106
  start-page: 59
  issue: 1
  year: 2007
  end-page: 70
  ident: CR56
  article-title: Learning generative visual models from few training examples: an incremental bayesian approach tested on 101 object categories
  publication-title: Computer Vision Image Understanding
– ident: CR103
– volume: 30
  start-page: 79
  issue: 1
  year: 2005
  end-page: 82
  ident: CR247
  article-title: Advantages of the mean absolute error (MAE) over the root mean square error (RMSE) in assessing average model performance
  publication-title: Climate Research
– ident: CR38
– ident: CR151
– ident: CR162
– ident: CR114
– ident: CR202
– volume: 20
  start-page: 985
  issue: 4
  year: 2017
  end-page: 996
  ident: CR127
  article-title: Scale-aware fast R-CNN for pedestrian detection
  publication-title: IEEE Transactions on Multimedia
– ident: CR120
– ident: CR258
– ident: CR275
– ident: CR70
– ident: CR148
– ident: CR177
– volume: 54
  start-page: 7405
  issue: 12
  year: 2016
  end-page: 7415
  ident: CR33
  article-title: Learning rotation-invariant convolutional neural networks for object detection in VHR optical remote sensing images
  publication-title: IEEE Trans Geosci Remote Sens
– ident: CR49
– ident: CR93
– volume: 115
  start-page: 211
  issue: 3
  year: 2015
  end-page: 252
  ident: CR195
  article-title: Imagenet large scale visual recognition challenge
  publication-title: Int J Comput Vis
– volume: 20
  start-page: 273
  issue: 3
  year: 1995
  end-page: 297
  ident: CR36
  article-title: Support vector machine
  publication-title: Mach Learn
– ident: CR154
– ident: CR111
– ident: CR219
– ident: CR243
– volume: 3
  start-page: 235
  issue: 4
  year: 1990
  end-page: 244
  ident: CR161
  article-title: Introduction to WordNet: an on-line lexical database
  publication-title: Int J Lexicogr
– ident: CR220
– ident: CR272
– ident: CR21
– ident: CR208
– volume: 6
  start-page: 417
  issue: 3
  year: 2018
  end-page: 431
  ident: CR158
  article-title: Towards real-time object detection on embedded systems
  publication-title: IEEE Transactions on Emerging Topics in Computing
– volume: 37
  start-page: 13
  issue: 1
  year: 2015
  end-page: 27
  ident: CR28
  article-title: Contextualizing object detection and classification
  publication-title: IEEE Transactions on Pattern Analysis and Machine Intelligence
– ident: CR231
– ident: CR75
– ident: CR15
– ident: CR225
– ident: CR266
– ident: CR283
– ident: CR32
– ident: CR189
– ident: CR81
– volume: 28
  start-page: 41
  issue: 1
  year: 1997
  end-page: 75
  ident: CR20
  article-title: Multitask learning
  publication-title: Mach Learn
– ident: CR64
– ident: CR214
– ident: CR105
– ident: CR26
– ident: CR99
– ident: CR277
– ident: CR143
– ident: CR160
– ident: CR43
– ident: CR203
– ident: CR14
– ident: CR288
– ident: CR2
– ident: CR37
– ident: CR53
– volume: 32
  start-page: 1627
  issue: 9
  year: 2010
  end-page: 1645
  ident: CR58
  article-title: Object detection with discriminatively trained part-based models
  publication-title: IEEE Transactions on Pattern Analysis and Machine Intelligence
– ident: CR226
– volume: 58
  start-page: 216
  year: 2016
  end-page: 226
  ident: CR244
  article-title: Multiscale patch-based contrast measure for small infrared target detection
  publication-title: Pattern Recogn
– volume: 111
  start-page: 98
  issue: 1
  year: 2015
  end-page: 136
  ident: CR51
  article-title: The pascal visual object classes challenge: a retrospective
  publication-title: Int J Comput Vis
– ident: CR133
– ident: CR265
– ident: CR82
– volume: 23
  start-page: 1499
  issue: 10
  year: 2016
  end-page: 1503
  ident: CR273
  article-title: Joint face detection and alignment using multitask cascaded convolutional networks
  publication-title: IEEE Signal Processing Letters
– ident: CR188
– volume: 40
  start-page: 1845
  issue: 8
  year: 2017
  end-page: 1859
  ident: CR257
  article-title: Faceness-net: face detection through deep facial part responses
  publication-title: IEEE Transactions on Pattern Analysis and Machine Intelligence
– ident: CR98
– volume: 42
  start-page: 60
  year: 2017
  end-page: 88
  ident: CR145
  article-title: A survey on deep learning in medical image analysis
  publication-title: Med Image Anal
– ident: CR104
– volume: 9
  start-page: 766
  issue: 4
  year: 2007
  end-page: 784
  ident: CR264
  article-title: A rule based technique for extraction of visual attention regions based on real-time clustering
  publication-title: IEEE Transactions on Multimedia
– ident: CR259
– ident: CR293
– volume: 28
  start-page: 2896
  issue: 10
  year: 2018
  end-page: 2907
  ident: CR102
  article-title: T-cnn: tubelets with convolutional neural networks for object detection from videos
  publication-title: IEEE Transactions on Circuits Systems for Video Technology
– ident: CR260
– ident: CR48
– ident: CR65
– ident: CR248
– ident: CR31
– ident: CR172
– volume: 28
  start-page: 594
  issue: 4
  year: 2006
  end-page: 611
  ident: CR55
  article-title: One-shot learning of object categories
  publication-title: IEEE Transactions on Pattern Analysis and Machine Intelligence
– ident: CR271
– ident: CR110
– ident: CR7
– ident: CR59
– ident: CR76
– ident: CR166
– ident: CR237
– ident: CR183
– ident: CR254
– volume: 46
  start-page: 2403
  issue: 5
  year: 2015
  end-page: 2412
  ident: CR87
  article-title: Logo-net: large-scale deep logo detection and brand recognition with deep region-based convolutional networks
  publication-title: IEEE Transactions on Pattern Analysis and Machine Intelligence
– volume: 521
  start-page: 436
  issue: 7553
  year: 2015
  end-page: 444
  ident: CR122
  article-title: Deep learning
  publication-title: Nature
– volume: 15
  start-page: 1929
  issue: 1
  year: 2014
  end-page: 1958
  ident: CR209
  article-title: Dropout: a simple way to prevent neural networks from overfitting
  publication-title: The Journal of Machine Learning Research
– volume: 104
  start-page: 154
  issue: 2
  year: 2013
  end-page: 171
  ident: CR224
  article-title: Selective search for object recognition
  publication-title: International Journal of Computer Vision (IJCV)
– ident: CR135
– ident: CR267
– volume: 29
  start-page: 947
  issue: 5
  year: 2018
  end-page: 952
  ident: CR139
  article-title: Using deep learning to detect small targets in infrared oversampling images
  publication-title: J Syst Eng Electron
– ident: CR54
– volume: 57
  start-page: 137
  issue: 2
  year: 2004
  end-page: 154
  ident: CR230
  article-title: Robust real-time face detection
  publication-title: International journal of computer vision
– ident: CR77
– ident: CR106
– ident: CR215
– ident: CR238
– ident: CR25
– ident: CR182
– ident: CR42
– ident: CR291
– volume: 33
  start-page: 353
  issue: 2
  year: 2010
  end-page: 367
  ident: CR149
  article-title: Learning to detect a salient object
  publication-title: IEEE Transactions on Pattern analysis and Machine Intelligence
– ident: CR101
– ident: CR71
– ident: CR256
– ident: CR19
– volume: 9
  start-page: 1735
  issue: 8
  year: 1997
  end-page: 1780
  ident: CR86
  article-title: Long short-term memory
  publication-title: Neural Comput
– ident: CR199
– ident: CR204
– ident: CR210
– ident: CR88
– ident: CR262
– ident: CR170
– ident: CR221
– ident: CR60
– ident: CR112
– ident: CR147
– ident: CR164
– volume: 11
  start-page: 620
  issue: 6
  year: 2019
  ident: CR234
  article-title: A hierarchical convolution neural network (CNN)-based ship target detection method in spaceborne SAR imagery
  publication-title: Remote Sens
– ident: CR198
– ident: CR100
– ident: CR18
– ident: CR66
– ident: CR47
– ident: CR72
– volume: 56
  start-page: 2337
  issue: 4
  year: 2017
  end-page: 2348
  ident: CR130
  article-title: Rotation-insensitive and context-augmented object detection in remote sensing images
  publication-title: IEEE Trans Geosci Remote Sens
– ident: CR89
– volume: 134
  start-page: 19
  issue: 1
  year: 2005
  end-page: 67
  ident: CR41
  article-title: A tutorial on the cross-entropy method
  publication-title: Ann Oper Res
– ident: CR30
– ident: CR171
– ident: CR261
– volume: 30
  start-page: 1958
  issue: 11
  year: 2008
  end-page: 1970
  ident: CR222
  article-title: 80 million tiny images: a large data set for nonparametric object and scene recognition
  publication-title: IEEE Transactions on Pattern Analysis and Machine Intelligence
– volume: 115
  start-page: 330
  issue: 3
  year: 2015
  end-page: 344
  ident: CR83
  article-title: Supercnn: a superpixelwise convolutional neural network for salient object detection
  publication-title: Int J Comput Vis
– ident: CR117
– ident: CR249
– ident: CR284
– volume: 35
  start-page: 1798
  issue: 8
  year: 2013
  end-page: 1828
  ident: CR8
  article-title: Representation learning: a review and new perspectives
  publication-title: IEEE Transactions on Pattern Analysis and Machine Intelligence
– ident: CR165
– ident: CR278
– ident: CR297
– ident: CR123
– ident: CR255
– ident: CR142
– ident: CR94
– ident: CR233
– ident: CR176
– ident: CR3
– ident: CR289
– ident: CR227
– volume: 88
  start-page: 303
  issue: 2
  year: 2010
  end-page: 338
  ident: CR52
  article-title: The pascal visual object classes (voc) challenge
  publication-title: Int J Comput Vis
– ident: CR134
– ident: CR13
– ident: CR187
– ident: CR159
– ident: CR216
– ident: CR250
– ident: CR24
– ident: CR128
– ident: CR292
– ident: CR125
– ident: CR205
– ident: CR211
– ident: CR4
– ident: CR131
– ident: CR240
– ident: CR263
– ident: CR228
– ident: CR192
– ident: CR12
– ident: CR119
– ident: CR186
– volume: 34
  start-page: 2189
  issue: 11
  year: 2012
  end-page: 2202
  ident: CR1
  article-title: Measuring the objectness of image windows
  publication-title: IEEE Transactions on Pattern Analysis and Machine Intelligence
– ident: CR35
– ident: CR29
– ident: CR61
– ident: CR84
– ident: CR140
– ident: CR163
– ident: CR295
– volume: 34
  start-page: 743
  issue: 4
  year: 2012
  end-page: 761
  ident: CR46
  article-title: Pedestrian detection: an evaluation of the state of the art
  publication-title: IEEE Transactions on Pattern Analysis and Machine Intelligence
– ident: CR174
– ident: CR96
– ident: CR67
– ident: CR50
– ident: CR157
– ident: CR136
– ident: CR9
– volume: 19
  start-page: 944
  issue: 5
  year: 2017
  end-page: 954
  ident: CR129
  article-title: Attentive contexts for object detection
  publication-title: IEEE Transactions on Multimedia
– ident: CR246
– ident: CR78
– ident: CR200
– ident: CR181
– ident: CR239
– ident: CR168
– ident: CR252
– ident: CR290
– ident: CR95
– ident: CR175
– volume: 119
  start-page: 3
  issue: 1
  year: 2016
  end-page: 22
  ident: CR251
  article-title: Sun database: exploring a large collection of scene categories
  publication-title: Int J Comput Vis
– ident: CR280
– ident: CR152
– ident: CR113
– ident: CR217
– ident: CR79
– ident: CR40
– ident: CR180
– ident: CR169
– ident: CR146
– ident: CR23
– ident: CR274
– volume: 7
  start-page: 1247
  issue: 3
  year: 2014
  end-page: 1250
  ident: CR22
  article-title: Root mean square error (RMSE) or mean absolute error (MAE)? Arguments against avoiding RMSE in the literature
  publication-title: Geosci Model Dev
– volume: 60
  start-page: 84
  issue: 6
  year: 2017
  end-page: 90
  ident: CR116
  article-title: Imagenet classification with deep convolutional neural networks
  publication-title: Commun ACM
– ident: CR124
– ident: CR197
– ident: CR73
– ident: CR212
– ident: CR206
– ident: CR90
– ident: CR17
– ident: CR285
– ident: CR118
– ident: CR268
– volume: 40
  start-page: 1452
  issue: 6
  year: 2018
  end-page: 1464
  ident: CR286
  article-title: Places: a 10 million image database for scene recognition
  publication-title: IEEE Transactions on Pattern Analysis and Machine Intelligence
– ident: CR34
– ident: CR223
– volume: 39
  start-page: 1476
  issue: 7
  year: 2016
  end-page: 1481
  ident: CR190
  article-title: Object detection networks on convolutional feature maps
  publication-title: IEEE transactions on pattern analysis and machine intelligence
– ident: CR107
– ident: CR62
– ident: CR141
– ident: CR279
– ident: CR296
– volume: 521
  start-page: 436
  issue: 7553
  year: 2015
  ident: 8976_CR122
  publication-title: Nature
  doi: 10.1038/nature14539
– ident: 8976_CR287
  doi: 10.1109/CVPR.2018.00062
– ident: 8976_CR69
  doi: 10.1109/ICASSP.2013.6638947
– ident: 8976_CR105
– ident: 8976_CR237
– ident: 8976_CR243
  doi: 10.1109/CVPR.2017.324
– ident: 8976_CR98
  doi: 10.1109/34.730558
– ident: 8976_CR73
– ident: 8976_CR29
  doi: 10.1109/ICCV.2017.440
– ident: 8976_CR275
  doi: 10.1109/CVPR.2017.474
– ident: 8976_CR241
  doi: 10.1109/ICCV.2009.5459207
– volume: 46
  start-page: 2403
  issue: 5
  year: 2015
  ident: 8976_CR87
  publication-title: IEEE Transactions on Pattern Analysis and Machine Intelligence
– ident: 8976_CR254
  doi: 10.21437/Interspeech.2013-552
– volume: 20
  start-page: 985
  issue: 4
  year: 2017
  ident: 8976_CR127
  publication-title: IEEE Transactions on Multimedia
– volume: 19
  start-page: 944
  issue: 5
  year: 2017
  ident: 8976_CR129
  publication-title: IEEE Transactions on Multimedia
  doi: 10.1109/TMM.2016.2642789
– ident: 8976_CR289
  doi: 10.1109/CVPR.2019.00094
– ident: 8976_CR94
  doi: 10.1007/978-3-319-10593-2_33
– ident: 8976_CR114
  doi: 10.1007/978-3-319-10602-1_47
– ident: 8976_CR278
  doi: 10.1109/BTAS.2017.8272675
– ident: 8976_CR163
– ident: 8976_CR108
  doi: 10.1109/ICME.2008.4607625
– ident: 8976_CR186
– ident: 8976_CR248
  doi: 10.1109/CVPR.2016.521
– ident: 8976_CR89
  doi: 10.1109/CVPR.2018.00745
– volume: 42
  start-page: 60
  year: 2017
  ident: 8976_CR145
  publication-title: Med Image Anal
  doi: 10.1016/j.media.2017.07.005
– ident: 8976_CR219
– volume: 60
  start-page: 84
  issue: 6
  year: 2017
  ident: 8976_CR116
  publication-title: Commun ACM
  doi: 10.1145/3065386
– volume: 60
  start-page: 91
  issue: 2
  year: 2004
  ident: 8976_CR155
  publication-title: Int J Comput Vis
  doi: 10.1023/B:VISI.0000029664.99615.94
– volume: 15
  start-page: 1929
  issue: 1
  year: 2014
  ident: 8976_CR209
  publication-title: The Journal of Machine Learning Research
– ident: 8976_CR118
  doi: 10.1109/ICRA.2011.5980382
– ident: 8976_CR32
  doi: 10.1007/978-3-030-01267-0_28
– volume: 34
  start-page: 2189
  issue: 11
  year: 2012
  ident: 8976_CR1
  publication-title: IEEE Transactions on Pattern Analysis and Machine Intelligence
  doi: 10.1109/TPAMI.2012.28
– ident: 8976_CR77
  doi: 10.1109/ICCV.2009.5459257
– ident: 8976_CR10
  doi: 10.1109/ICCV.2017.593
– ident: 8976_CR67
– ident: 8976_CR231
– ident: 8976_CR220
  doi: 10.1109/ICCV.2015.221
– ident: 8976_CR199
  doi: 10.1109/ICCV.2017.212
– ident: 8976_CR152
  doi: 10.1109/CVPR.2015.7298965
– ident: 8976_CR103
  doi: 10.1109/CVPR.2016.95
– ident: 8976_CR282
  doi: 10.1109/CVPR.2016.451
– ident: 8976_CR104
  doi: 10.1109/CVPR.2014.223
– volume: 6
  start-page: 417
  issue: 3
  year: 2018
  ident: 8976_CR158
  publication-title: IEEE Transactions on Emerging Topics in Computing
  doi: 10.1109/TETC.2016.2593643
– volume: 30
  start-page: 79
  issue: 1
  year: 2005
  ident: 8976_CR247
  publication-title: Climate Research
  doi: 10.3354/cr030079
– ident: 8976_CR175
  doi: 10.1109/ICCV.2019.00970
– ident: 8976_CR296
  doi: 10.1007/978-3-319-10602-1_26
– ident: 8976_CR204
  doi: 10.1109/CVPR.2018.00377
– volume: 7
  start-page: 1247
  issue: 3
  year: 2014
  ident: 8976_CR22
  publication-title: Geosci Model Dev
  doi: 10.5194/gmd-7-1247-2014
– volume: 9
  start-page: 766
  issue: 4
  year: 2007
  ident: 8976_CR264
  publication-title: IEEE Transactions on Multimedia
  doi: 10.1109/TMM.2007.893351
– ident: 8976_CR197
– ident: 8976_CR203
– ident: 8976_CR226
– ident: 8976_CR63
  doi: 10.1109/ICCV.2015.296
– ident: 8976_CR258
  doi: 10.1109/CVPR.2017.643
– volume: 29
  start-page: 947
  issue: 5
  year: 2018
  ident: 8976_CR139
  publication-title: J Syst Eng Electron
  doi: 10.21629/JSEE.2018.05.07
– ident: 8976_CR284
  doi: 10.1109/CVPR.2015.7298731
– ident: 8976_CR18
  doi: 10.1007/978-3-030-01264-9_9
– ident: 8976_CR169
– ident: 8976_CR74
  doi: 10.1109/ICME.2019.00009
– ident: 8976_CR266
  doi: 10.1007/978-3-319-10590-1_53
– ident: 8976_CR38
– ident: 8976_CR267
  doi: 10.1109/CVPR.2010.5539957
– ident: 8976_CR277
  doi: 10.1109/CVPR.2018.00442
– ident: 8976_CR47
  doi: 10.1109/ICCV.2019.00667
– ident: 8976_CR60
  doi: 10.1109/CVPR.2018.00724
– volume: 39
  start-page: 1476
  issue: 7
  year: 2016
  ident: 8976_CR190
  publication-title: IEEE transactions on pattern analysis and machine intelligence
  doi: 10.1109/TPAMI.2016.2601099
– ident: 8976_CR184
  doi: 10.1007/978-3-030-20887-5_34
– ident: 8976_CR228
  doi: 10.1109/ICCV.2009.5459183
– ident: 8976_CR141
– volume: 134
  start-page: 19
  issue: 1
  year: 2005
  ident: 8976_CR41
  publication-title: Ann Oper Res
  doi: 10.1007/s10479-005-5724-z
– ident: 8976_CR185
  doi: 10.1007/978-3-319-46493-0_32
– ident: 8976_CR260
  doi: 10.1007/978-3-319-16811-1_34
– ident: 8976_CR216
  doi: 10.1109/CVPR.2019.00293
– volume: 65
  start-page: 417
  issue: 1
  year: 2016
  ident: 8976_CR245
  publication-title: CIRP Annals-Manufacturing Technology
  doi: 10.1016/j.cirp.2016.04.072
– ident: 8976_CR17
  doi: 10.1117/12.2304811
– volume: 23
  start-page: 1499
  issue: 10
  year: 2016
  ident: 8976_CR273
  publication-title: IEEE Signal Processing Letters
  doi: 10.1109/LSP.2016.2603342
– ident: 8976_CR27
– ident: 8976_CR61
– ident: 8976_CR293
  doi: 10.1109/CVPR.2017.441
– ident: 8976_CR157
  doi: 10.1007/978-3-030-01264-9_8
– ident: 8976_CR49
  doi: 10.1007/978-3-642-15555-0_42
– ident: 8976_CR93
  doi: 10.1109/CVPR.2017.351
– volume: 9
  start-page: 1735
  issue: 8
  year: 1997
  ident: 8976_CR86
  publication-title: Neural Comput
  doi: 10.1162/neco.1997.9.8.1735
– ident: 8976_CR39
– ident: 8976_CR143
  doi: 10.1007/978-3-319-10602-1_48
– ident: 8976_CR265
  doi: 10.5244/C.30.15
– ident: 8976_CR37
  doi: 10.1109/CVPR.2016.343
– volume: 56
  start-page: 2337
  issue: 4
  year: 2017
  ident: 8976_CR130
  publication-title: IEEE Trans Geosci Remote Sens
  doi: 10.1109/TGRS.2017.2778300
– ident: 8976_CR68
– volume: 3
  start-page: 235
  issue: 4
  year: 1990
  ident: 8976_CR161
  publication-title: Int J Lexicogr
  doi: 10.1093/ijl/3.4.235
– ident: 8976_CR167
– ident: 8976_CR111
  doi: 10.1109/CVPR.2017.557
– ident: 8976_CR62
  doi: 10.1109/CVPR.2019.00720
– ident: 8976_CR97
– ident: 8976_CR174
  doi: 10.1109/CVPR.2018.00647
– ident: 8976_CR182
  doi: 10.1109/ICCV.2007.4408986
– volume: 323
  start-page: 533
  issue: 6088
  year: 1986
  ident: 8976_CR194
  publication-title: Nature
  doi: 10.1038/323533a0
– ident: 8976_CR180
  doi: 10.1109/CVPR.2006.119
– ident: 8976_CR188
  doi: 10.1109/CVPR.2017.690
– ident: 8976_CR91
  doi: 10.1109/CVPR.2017.65
– ident: 8976_CR221
  doi: 10.1109/ICCV.2019.00972
– ident: 8976_CR4
  doi: 10.1109/CVPR.2014.49
– ident: 8976_CR11
– ident: 8976_CR191
– volume: 32
  start-page: 1627
  issue: 9
  year: 2010
  ident: 8976_CR58
  publication-title: IEEE Transactions on Pattern Analysis and Machine Intelligence
  doi: 10.1109/TPAMI.2009.167
– ident: 8976_CR196
  doi: 10.1109/CVPR.2018.00474
– ident: 8976_CR179
– volume: 88
  start-page: 303
  issue: 2
  year: 2010
  ident: 8976_CR52
  publication-title: Int J Comput Vis
  doi: 10.1007/s11263-009-0275-4
– ident: 8976_CR201
  doi: 10.1109/ICCV.2017.368
– ident: 8976_CR210
  doi: 10.1109/IROS.2012.6385773
– ident: 8976_CR269
  doi: 10.1007/978-3-319-46478-7_22
– ident: 8976_CR256
– ident: 8976_CR35
  doi: 10.1109/CVPR.2016.350
– ident: 8976_CR5
  doi: 10.1609/aaai.v33i01.33018094
– ident: 8976_CR294
– ident: 8976_CR148
  doi: 10.1007/978-3-030-01252-6_24
– volume: 9
  start-page: 1823
  issue: 8
  year: 2008
  ident: 8976_CR6
  publication-title: J Mach Learn Res
– ident: 8976_CR112
  doi: 10.1109/CVPR.2016.98
– ident: 8976_CR198
  doi: 10.1007/978-3-319-49409-8_69
– ident: 8976_CR240
  doi: 10.1007/978-3-319-15554-8_73
– ident: 8976_CR291
  doi: 10.1109/CVPR.2018.00753
– ident: 8976_CR106
  doi: 10.1007/978-3-319-47157-0_20
– ident: 8976_CR7
  doi: 10.1109/CVPR.2016.314
– ident: 8976_CR262
  doi: 10.1109/CVPR.2017.75
– ident: 8976_CR178
– ident: 8976_CR72
  doi: 10.1007/978-3-319-10584-0_23
– ident: 8976_CR172
  doi: 10.1109/CVPR.2019.00091
– ident: 8976_CR249
– ident: 8976_CR227
  doi: 10.1109/CVPR.2018.00914
– ident: 8976_CR78
  doi: 10.1007/978-0-387-84858-7_14
– ident: 8976_CR85
– ident: 8976_CR159
  doi: 10.1109/CVPR.2017.639
– ident: 8976_CR297
  doi: 10.1109/CVPR.2018.00907
– ident: 8976_CR71
  doi: 10.1109/CVPR.2018.00633
– volume: 8
  start-page: 813
  issue: 5
  year: 2018
  ident: 8976_CR193
  publication-title: Appl Sci
  doi: 10.3390/app8050813
– ident: 8976_CR123
  doi: 10.1109/5.726791
– ident: 8976_CR156
– ident: 8976_CR211
  doi: 10.5244/C.28.82
– ident: 8976_CR276
  doi: 10.1007/978-3-030-01219-9_39
– volume: 106
  start-page: 59
  issue: 1
  year: 2007
  ident: 8976_CR56
  publication-title: Computer Vision Image Understanding
  doi: 10.1016/j.cviu.2005.09.012
– ident: 8976_CR261
– ident: 8976_CR21
  doi: 10.1007/978-3-030-01258-8_15
– ident: 8976_CR200
  doi: 10.1007/978-3-319-23528-8_9
– ident: 8976_CR14
  doi: 10.1007/978-3-319-46493-0_22
– ident: 8976_CR9
  doi: 10.1007/978-3-319-48881-3_56
– ident: 8976_CR126
  doi: 10.1109/CVPR.2015.7299170
– ident: 8976_CR140
  doi: 10.1109/ICIP.2002.1038171
– ident: 8976_CR96
– volume: 58
  start-page: 216
  year: 2016
  ident: 8976_CR244
  publication-title: Pattern Recogn
  doi: 10.1016/j.patcog.2016.04.002
– ident: 8976_CR250
– ident: 8976_CR162
– ident: 8976_CR288
  doi: 10.1109/CVPR.2018.00062
– ident: 8976_CR181
  doi: 10.1145/2847263.2847265
– ident: 8976_CR235
  doi: 10.1109/CVPR.2015.7298938
– ident: 8976_CR212
  doi: 10.1609/aaai.v31i1.11231
– volume: 35
  start-page: 1798
  issue: 8
  year: 2013
  ident: 8976_CR8
  publication-title: IEEE Transactions on Pattern Analysis and Machine Intelligence
  doi: 10.1109/TPAMI.2013.50
– ident: 8976_CR119
– ident: 8976_CR70
– volume: 111
  start-page: 98
  issue: 1
  year: 2015
  ident: 8976_CR51
  publication-title: Int J Comput Vis
  doi: 10.1007/s11263-014-0733-5
– ident: 8976_CR133
  doi: 10.1109/ICCV.2019.00615
– ident: 8976_CR44
  doi: 10.1109/CVPRW.2009.5206532
– ident: 8976_CR255
  doi: 10.1109/ICMEW.2015.7169816
– volume: 104
  start-page: 154
  issue: 2
  year: 2013
  ident: 8976_CR224
  publication-title: International Journal of Computer Vision (IJCV)
  doi: 10.1007/s11263-013-0620-5
– ident: 8976_CR166
  doi: 10.1109/ICPR.2006.479
– ident: 8976_CR242
  doi: 10.1109/CVPR.2018.00811
– ident: 8976_CR125
– ident: 8976_CR271
  doi: 10.1109/CVPR.2016.618
– ident: 8976_CR15
  doi: 10.1109/CVPR.2019.00958
– ident: 8976_CR24
  doi: 10.1109/ICCV.2015.312
– ident: 8976_CR64
  doi: 10.1109/CVPRW.2018.00215
– ident: 8976_CR154
– ident: 8976_CR160
– ident: 8976_CR76
  doi: 10.1109/TPAMI.2016.2578328
– ident: 8976_CR263
– ident: 8976_CR79
  doi: 10.1109/ICCV.2019.00502
– ident: 8976_CR168
  doi: 10.1109/CVPR.2014.222
– ident: 8976_CR137
– ident: 8976_CR53
– volume: 40
  start-page: 1845
  issue: 8
  year: 2017
  ident: 8976_CR257
  publication-title: IEEE Transactions on Pattern Analysis and Machine Intelligence
  doi: 10.1109/TPAMI.2017.2738644
– ident: 8976_CR138
  doi: 10.1109/CVPR.2015.7298958
– ident: 8976_CR171
  doi: 10.1109/CVPR.2015.7298854
– ident: 8976_CR292
  doi: 10.1109/ICCV.2017.52
– ident: 8976_CR3
– ident: 8976_CR246
– ident: 8976_CR110
  doi: 10.1007/978-3-319-46726-9_31
– volume: 40
  start-page: 1452
  issue: 6
  year: 2018
  ident: 8976_CR286
  publication-title: IEEE Transactions on Pattern Analysis and Machine Intelligence
  doi: 10.1109/TPAMI.2017.2723009
– ident: 8976_CR189
– ident: 8976_CR150
  doi: 10.1007/978-3-319-46448-0_2
– ident: 8976_CR239
– ident: 8976_CR202
  doi: 10.1109/CVPR.2016.89
– ident: 8976_CR42
  doi: 10.1109/CVPR.2009.5206848
– ident: 8976_CR80
  doi: 10.1109/ICCV.2017.322
– ident: 8976_CR132
  doi: 10.1109/ICCV.2019.00671
– ident: 8976_CR113
– ident: 8976_CR192
  doi: 10.1109/CVPR.2013.417
– ident: 8976_CR54
  doi: 10.1109/IVS.2016.7535375
– ident: 8976_CR144
  doi: 10.1109/CVPR.2017.106
– ident: 8976_CR57
  doi: 10.1109/CVPR.2008.4587597
– volume: 34
  start-page: 743
  issue: 4
  year: 2012
  ident: 8976_CR46
  publication-title: IEEE Transactions on Pattern Analysis and Machine Intelligence
  doi: 10.1109/TPAMI.2011.155
– ident: 8976_CR117
  doi: 10.1109/ICCV.2015.285
– volume: 33
  start-page: 353
  issue: 2
  year: 2010
  ident: 8976_CR149
  publication-title: IEEE Transactions on Pattern analysis and Machine Intelligence
  doi: 10.1109/TPAMI.2010.70
– ident: 8976_CR34
  doi: 10.1109/CVPR.2017.195
– ident: 8976_CR268
  doi: 10.1109/ICCV.2011.6126474
– ident: 8976_CR31
  doi: 10.1109/CVPR.2017.691
– ident: 8976_CR187
  doi: 10.1109/CVPR.2016.91
– ident: 8976_CR120
  doi: 10.1007/978-3-030-01264-9_45
– ident: 8976_CR101
  doi: 10.1109/FG.2017.82
– volume: 28
  start-page: 41
  issue: 1
  year: 1997
  ident: 8976_CR20
  publication-title: Mach Learn
  doi: 10.1023/A:1007379606734
– ident: 8976_CR217
– ident: 8976_CR218
  doi: 10.1109/IVS.2018.8500504
– ident: 8976_CR19
  doi: 10.1109/TPAMI.2011.231
– volume: 28
  start-page: 594
  issue: 4
  year: 2006
  ident: 8976_CR55
  publication-title: IEEE Transactions on Pattern Analysis and Machine Intelligence
  doi: 10.1109/TPAMI.2006.79
– ident: 8976_CR13
– ident: 8976_CR281
  doi: 10.1109/CVPR.2018.00609
– ident: 8976_CR229
  doi: 10.1109/CVPR.2001.990517
– ident: 8976_CR30
– ident: 8976_CR65
  doi: 10.1109/ICCV.2015.169
– ident: 8976_CR82
  doi: 10.1109/CVPR.2016.90
– ident: 8976_CR135
  doi: 10.1007/978-3-030-01240-3_21
– ident: 8976_CR165
  doi: 10.1109/ICCV.2017.522
– ident: 8976_CR206
– ident: 8976_CR124
– ident: 8976_CR208
  doi: 10.1109/ICCV.2003.1238663
– ident: 8976_CR272
  doi: 10.1109/CVPR.2018.00941
– ident: 8976_CR225
  doi: 10.1109/ICCV.2011.6126456
– ident: 8976_CR66
  doi: 10.1109/CVPR.2014.81
– ident: 8976_CR183
– ident: 8976_CR215
  doi: 10.1109/CVPR.2014.220
– ident: 8976_CR285
– ident: 8976_CR109
– volume: 30
  start-page: 1958
  issue: 11
  year: 2008
  ident: 8976_CR222
  publication-title: IEEE Transactions on Pattern Analysis and Machine Intelligence
  doi: 10.1109/TPAMI.2008.128
– ident: 8976_CR223
  doi: 10.1109/ICCV.2017.54
– volume: 28
  start-page: 2896
  issue: 10
  year: 2018
  ident: 8976_CR102
  publication-title: IEEE Transactions on Circuits Systems for Video Technology
  doi: 10.1109/TCSVT.2017.2736553
– ident: 8976_CR176
– ident: 8976_CR16
  doi: 10.1109/CVPR.2018.00644
– ident: 8976_CR81
  doi: 10.1007/978-3-319-10578-9_23
– ident: 8976_CR151
  doi: 10.1109/CVPR.2018.00730
– ident: 8976_CR283
  doi: 10.1609/aaai.v33i01.33019259
– ident: 8976_CR131
  doi: 10.1109/CVPR.2019.01082
– ident: 8976_CR236
  doi: 10.1007/978-3-319-46493-0_50
– ident: 8976_CR115
– ident: 8976_CR253
– ident: 8976_CR2
– ident: 8976_CR75
  doi: 10.1109/CVPR.2017.207
– volume: 37
  start-page: 13
  issue: 1
  year: 2015
  ident: 8976_CR28
  publication-title: IEEE Transactions on Pattern Analysis and Machine Intelligence
  doi: 10.1109/TPAMI.2014.2343217
– ident: 8976_CR279
  doi: 10.1109/ICCV.2017.30
– ident: 8976_CR128
  doi: 10.1109/CVPR.2017.211
– ident: 8976_CR12
  doi: 10.1142/9789812797926_0003
– ident: 8976_CR170
  doi: 10.1109/ICCV.2013.257
– volume: 115
  start-page: 211
  issue: 3
  year: 2015
  ident: 8976_CR195
  publication-title: Int J Comput Vis
  doi: 10.1007/s11263-015-0816-y
– volume: 16
  start-page: 310
  issue: 2
  year: 2018
  ident: 8976_CR232
  publication-title: IEEE Geosci Remote Sens Lett
  doi: 10.1109/LGRS.2018.2872355
– ident: 8976_CR295
  doi: 10.1109/ICCV.2017.444
– ident: 8976_CR121
  doi: 10.1109/CVPR.2006.68
– ident: 8976_CR50
  doi: 10.1109/CVPR.2014.276
– volume: 20
  start-page: 273
  issue: 3
  year: 1995
  ident: 8976_CR36
  publication-title: Mach Learn
– ident: 8976_CR213
  doi: 10.1109/CVPR.2015.7298594
– volume: 54
  start-page: 7405
  issue: 12
  year: 2016
  ident: 8976_CR33
  publication-title: IEEE Trans Geosci Remote Sens
  doi: 10.1109/TGRS.2016.2601622
– ident: 8976_CR134
  doi: 10.1109/CVPR.2017.553
– ident: 8976_CR280
– ident: 8976_CR88
– ident: 8976_CR99
  doi: 10.4467/20838476SI.16.004.6185
– ident: 8976_CR259
  doi: 10.1109/ICCV.2019.00975
– ident: 8976_CR100
  doi: 10.1007/978-3-030-01264-9_48
– volume: 119
  start-page: 3
  issue: 1
  year: 2016
  ident: 8976_CR251
  publication-title: Int J Comput Vis
  doi: 10.1007/s11263-014-0748-y
– ident: 8976_CR164
– ident: 8976_CR142
  doi: 10.1109/ICCV.2017.324
– ident: 8976_CR274
  doi: 10.1007/978-3-319-46475-6_28
– ident: 8976_CR95
  doi: 10.1007/978-3-319-46723-8_28
– ident: 8976_CR25
– volume: 55
  start-page: 2486
  issue: 5
  year: 2017
  ident: 8976_CR153
  publication-title: IEEE Trans Geosci Remote Sens
  doi: 10.1109/TGRS.2016.2645610
– ident: 8976_CR270
– ident: 8976_CR136
– ident: 8976_CR26
  doi: 10.1609/aaai.v32i1.11716
– ident: 8976_CR214
  doi: 10.1109/CVPR.2016.308
– ident: 8976_CR92
  doi: 10.1109/CVPR.2017.243
– ident: 8976_CR205
  doi: 10.1007/978-3-030-11009-3_11
– ident: 8976_CR84
  doi: 10.1109/CVPR.2019.00300
– ident: 8976_CR207
– ident: 8976_CR40
  doi: 10.1109/CVPR.2005.177
– ident: 8976_CR252
  doi: 10.1109/CVPR.2017.634
– ident: 8976_CR43
– ident: 8976_CR59
– ident: 8976_CR23
  doi: 10.1109/ACPR.2013.33
– ident: 8976_CR173
  doi: 10.1109/TGRS.2019.2899955
– ident: 8976_CR90
  doi: 10.1109/CVPR.2017.166
– ident: 8976_CR177
  doi: 10.1109/CVPR.2015.7299188
– ident: 8976_CR147
  doi: 10.1007/s11263-019-01247-4
– ident: 8976_CR238
  doi: 10.1007/978-3-030-01261-8_33
– ident: 8976_CR45
  doi: 10.1109/CVPRW.2009.5206631
– volume: 115
  start-page: 330
  issue: 3
  year: 2015
  ident: 8976_CR83
  publication-title: Int J Comput Vis
  doi: 10.1007/s11263-015-0822-0
– ident: 8976_CR146
  doi: 10.1007/978-3-030-01246-5_2
– ident: 8976_CR290
  doi: 10.1109/CVPR.2019.00237
– volume: 11
  start-page: 620
  issue: 6
  year: 2019
  ident: 8976_CR234
  publication-title: Remote Sens
  doi: 10.3390/rs11060620
– ident: 8976_CR107
  doi: 10.1007/978-3-030-01228-1_15
– ident: 8976_CR48
– volume: 57
  start-page: 137
  issue: 2
  year: 2004
  ident: 8976_CR230
  publication-title: International journal of computer vision
  doi: 10.1023/B:VISI.0000013087.49260.fb
– ident: 8976_CR233
  doi: 10.1109/CVPR.2018.00136
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Snippet With the rapid development of deep learning techniques, deep convolutional neural networks (DCNNs) have become more important for object detection. Compared...
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SubjectTerms Algorithms
Artificial neural networks
Computer Communication Networks
Computer networks
Computer Science
Data Structures and Information Theory
Deep learning
Machine learning
Meteorological satellites
Multimedia Information Systems
Neural networks
Object recognition
Special Purpose and Application-Based Systems
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Title A review of object detection based on deep learning
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