A Visual Detection Method for Foreign Objects in Power Lines Based on Mask R-CNN
The high-voltage power lines and transmission towers are large in volume, large in number, and wide in coverage, so they are easily attached to foreign objects, which may cause failure of the transmission line. The existing object detection methods are susceptible to weather and environmental factor...
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| Published in | International journal of ambient computing and intelligence Vol. 11; no. 1; pp. 34 - 47 |
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| Main Authors | , , |
| Format | Journal Article |
| Language | English |
| Published |
Hershey
IGI Global
01.01.2020
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| Subjects | |
| Online Access | Get full text |
| ISSN | 1941-6237 1941-6245 |
| DOI | 10.4018/IJACI.2020010102 |
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| Abstract | The high-voltage power lines and transmission towers are large in volume, large in number, and wide in coverage, so they are easily attached to foreign objects, which may cause failure of the transmission line. The existing object detection methods are susceptible to weather and environmental factors, and the use of neural networks for target detection can achieve good results. Therefore, this article uses MASK R-CNN as the basic network detection method for detecting foreign objects in the transmission network. The experimental results show that compared with the traditional target detection method, the method adopted in this article has achieved good results in the speed, efficiency, and recognition precision of foreign object detection. In the future, image processing operations can be performed for complex backgrounds of transmission lines to improve recognition effect. |
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| AbstractList | The high-voltage power lines and transmission towers are large in volume, large in number, and wide in coverage, so they are easily attached to foreign objects, which may cause failure of the transmission line. The existing object detection methods are susceptible to weather and environmental factors, and the use of neural networks for target detection can achieve good results. Therefore, this article uses MASK R-CNN as the basic network detection method for detecting foreign objects in the transmission network. The experimental results show that compared with the traditional target detection method, the method adopted in this article has achieved good results in the speed, efficiency, and recognition precision of foreign object detection. In the future, image processing operations can be performed for complex backgrounds of transmission lines to improve recognition effect. |
| Audience | Academic |
| Author | Chen, Wenxiang Li, Yingna Li, Chuan |
| AuthorAffiliation | Key Laboratory of Application of Computer Technology of the Yunnan Province, KMUST, China |
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| Cites_doi | 10.1109/CVPRW.2018.00045 10.1109/LGRS.2018.2882551 10.1109/IVCNZ.2016.7804438 10.1109/TIP.2017.2756825 10.1016/j.aei.2018.12.005 10.1109/TII.2012.2187913 10.3390.10040415 10.1007/s00521-016-2645-5 10.1109/5.726791 10.1109/ICCV.2017.322 10.1109/CVPR.2014.81 |
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| DOI | 10.4018/IJACI.2020010102 |
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| References | IJACI.2020010102-10 IJACI.2020010102-32 IJACI.2020010102-33 IJACI.2020010102-2 IJACI.2020010102-12 Q. C.Peng (IJACI.2020010102-31) 2019; 59 IJACI.2020010102-34 IJACI.2020010102-13 IJACI.2020010102-0 W.Bo (IJACI.2020010102-4) 2014 J. L.Wu (IJACI.2020010102-21) 2018; 48 J. P.Huang (IJACI.2020010102-28) 2019; 56 Y.LeCun (IJACI.2020010102-9) 1998; 86 IJACI.2020010102-18 Y. H.Zhu (IJACI.2020010102-7) 2017 Q.Gao (IJACI.2020010102-19) 2015; 52 X. F.Qi (IJACI.2020010102-1) 2017; 13 HCheng (IJACI.2020010102-30) 2019; 42 IJACI.2020010102-8 S. C.Satapathy (IJACI.2020010102-20) 2018; 29 R. H.Meng (IJACI.2020010102-26) 2018; 55 IJACI.2020010102-6 IJACI.2020010102-5 J. S.Yuan (IJACI.2020010102-17) 2014; 52 IJACI.2020010102-22 IJACI.2020010102-24 R.Girdhar (IJACI.2020010102-23) 2018 W.Liu (IJACI.2020010102-15) 2016 Q. G.Wu (IJACI.2020010102-16) 2012 Q.Wu (IJACI.2020010102-3) 2003 J. N.Li (IJACI.2020010102-25) 2018; 20 DWan (IJACI.2020010102-27) 2018; 42 G.Lin (IJACI.2020010102-29) 2019; 39 R.Girshick (IJACI.2020010102-11) 2015 J.Redmon (IJACI.2020010102-14) 2016 |
| References_xml | – ident: IJACI.2020010102-24 doi: 10.1109/CVPRW.2018.00045 – year: 2017 ident: IJACI.2020010102-7 publication-title: Research on visual inspection method of patrol robot – volume: 52 start-page: 106 issue: 7 year: 2014 ident: IJACI.2020010102-17 article-title: Identification and Location of Insulator Video Image Based on ASIFT Algorithm. publication-title: Journal of Electric Measurement & Instrumentation – ident: IJACI.2020010102-32 doi: 10.1109/LGRS.2018.2882551 – year: 2003 ident: IJACI.2020010102-3 article-title: Analysis of transient grounding resistance under pulsed discharging current with parallel FDTD algorithm. publication-title: Proceedings of the International Symposium on Antennas IEEE – volume: 42 start-page: 111 issue: 1 year: 2018 ident: IJACI.2020010102-27 article-title: A method for detection of foreign objects in transmission lines based on visual saliency analysis. publication-title: Television Technology – volume: 13 start-page: 158 year: 2017 ident: IJACI.2020010102-1 article-title: Discussion on the inspection method of transmission lines. publication-title: Electronic World – year: 2014 ident: IJACI.2020010102-4 publication-title: Research and development of backstage management and inspection system based on transmission line inspection robot – volume: 39 start-page: 213 issue: 5 year: 2019 ident: IJACI.2020010102-29 article-title: Multi-target detection and location of transmission line inspection image based on improved Faster-RCNN. publication-title: Electric Power Automation Equipment – ident: IJACI.2020010102-6 doi: 10.1109/IVCNZ.2016.7804438 – ident: IJACI.2020010102-22 doi: 10.1109/TIP.2017.2756825 – ident: IJACI.2020010102-33 doi: 10.1016/j.aei.2018.12.005 – ident: IJACI.2020010102-8 – ident: IJACI.2020010102-12 – volume: 59 start-page: 53 issue: 2 year: 2019 ident: IJACI.2020010102-31 article-title: Object Recognition and Location Based on Mask R-CNN. publication-title: Qinghua Daxue Xuebao. Ziran Kexue Ban – ident: IJACI.2020010102-0 – ident: IJACI.2020010102-18 – volume: 20 start-page: 985 issue: 4 year: 2018 ident: IJACI.2020010102-25 article-title: Scale-Aware Fast R-CNN for Pedestrian Detection. publication-title: IEEE Transactions on Multimedia – year: 2012 ident: IJACI.2020010102-16 publication-title: Research on component edge extraction algorithm in complex background power line image – start-page: 350 year: 2018 ident: IJACI.2020010102-23 article-title: Detect-and-track: Efficient pose estimation in videos. publication-title: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition – ident: IJACI.2020010102-2 doi: 10.1109/TII.2012.2187913 – ident: IJACI.2020010102-5 doi: 10.3390.10040415 – volume: 56 start-page: 319 issue: 2 year: 2019 ident: IJACI.2020010102-28 article-title: Multi-scale Faster-RCNN Detection Algorithm for Small Targets. publication-title: Jisuanji Yanjiu Yu Fazhan – start-page: 21 year: 2016 ident: IJACI.2020010102-15 article-title: Ssd: Single shot multibox detector. publication-title: Proceedings of the European conference on computer vision – volume: 29 start-page: 1285 issue: 12 year: 2018 ident: IJACI.2020010102-20 article-title: Multi-level image thresholding using Otsu and chaotic bat algorithm. publication-title: Neural Computing & Applications doi: 10.1007/s00521-016-2645-5 – volume: 86 start-page: 2278 issue: 11 year: 1998 ident: IJACI.2020010102-9 article-title: Gradient-based learning applied to document recognition. publication-title: Proceedings of the IEEE doi: 10.1109/5.726791 – volume: 55 start-page: 1 issue: 1 year: 2018 ident: IJACI.2020010102-26 article-title: A Fusion Steganographic Algorithm Based on Faster R-CNN. publication-title: Computers Materials & Continua – ident: IJACI.2020010102-13 doi: 10.1109/ICCV.2017.322 – volume: 52 start-page: 117 issue: 3 year: 2015 ident: IJACI.2020010102-19 article-title: Image Feature Description Based on Difference and Its Application in Insulator Identification. publication-title: Journal of Electric Measurement & Instrumentation – start-page: 779 year: 2016 ident: IJACI.2020010102-14 article-title: You only look once: Unified, real-time object detection. publication-title: Proceedings of the IEEE conference on computer vision and pattern recognition – volume: 42 start-page: 106 issue: 2 year: 2019 ident: IJACI.2020010102-30 article-title: Insulator Recognition in Aerial Images Based on Faster R-CNN. publication-title: Modern Electronic Technology – ident: IJACI.2020010102-10 doi: 10.1109/CVPR.2014.81 – start-page: 1440 year: 2015 ident: IJACI.2020010102-11 article-title: Fast r-cnn. publication-title: Proceedings of the IEEE international conference on computer vision – volume: 48 start-page: 39 issue: 11 year: 2018 ident: IJACI.2020010102-21 article-title: Research on Ship Target Detection Based on Mask R-CNN. publication-title: Wuxiandian Gongcheng – ident: IJACI.2020010102-34 |
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| Snippet | The high-voltage power lines and transmission towers are large in volume, large in number, and wide in coverage, so they are easily attached to foreign... |
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| SubjectTerms | Equipment and supplies Image processing Methods Neural networks Power lines |
| Title | A Visual Detection Method for Foreign Objects in Power Lines Based on Mask R-CNN |
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