Recent advances in small object detection based on deep learning: A review
Small object detection is a challenging problem in computer vision. It has been widely applied in defense military, transportation, industry, etc. To facilitate in-depth understanding of small object detection, we comprehensively review the existing small object detection methods based on deep learn...
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| Published in | Image and vision computing Vol. 97; p. 103910 |
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| Main Authors | , , |
| Format | Journal Article |
| Language | English |
| Published |
Elsevier B.V
01.05.2020
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| Subjects | |
| Online Access | Get full text |
| ISSN | 0262-8856 1872-8138 |
| DOI | 10.1016/j.imavis.2020.103910 |
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| Abstract | Small object detection is a challenging problem in computer vision. It has been widely applied in defense military, transportation, industry, etc. To facilitate in-depth understanding of small object detection, we comprehensively review the existing small object detection methods based on deep learning from five aspects, including multi-scale feature learning, data augmentation, training strategy, context-based detection and GAN-based detection. Then, we thoroughly analyze the performance of some typical small object detection algorithms on popular datasets, such as MS-COCO, PASCAL-VOC. Finally, the possible research directions in the future are pointed out from five perspectives: emerging small object detection datasets and benchmarks, multi-task joint learning and optimization, information transmission, weakly supervised small object detection methods and framework for small object detection task. |
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| AbstractList | Small object detection is a challenging problem in computer vision. It has been widely applied in defense military, transportation, industry, etc. To facilitate in-depth understanding of small object detection, we comprehensively review the existing small object detection methods based on deep learning from five aspects, including multi-scale feature learning, data augmentation, training strategy, context-based detection and GAN-based detection. Then, we thoroughly analyze the performance of some typical small object detection algorithms on popular datasets, such as MS-COCO, PASCAL-VOC. Finally, the possible research directions in the future are pointed out from five perspectives: emerging small object detection datasets and benchmarks, multi-task joint learning and optimization, information transmission, weakly supervised small object detection methods and framework for small object detection task. |
| ArticleNumber | 103910 |
| Author | Wu, Yiquan Zhou, Fei Tong, Kang |
| Author_xml | – sequence: 1 givenname: Kang surname: Tong fullname: Tong, Kang email: tkangcv@nuaa.edu.cn – sequence: 2 givenname: Yiquan surname: Wu fullname: Wu, Yiquan email: mltd2099@163.com – sequence: 3 givenname: Fei surname: Zhou fullname: Zhou, Fei email: F.zhouip@nuaa.edu.cn |
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| Title | Recent advances in small object detection based on deep learning: A review |
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