Recent advances in small object detection based on deep learning: A review

• Published in: Image and vision computing Vol. 97; p. 103910
• Main Authors: Tong, Kang, Wu, Yiquan, Zhou, Fei
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.05.2020
• Subjects: Computer vision; Convolutional neural networks; Deep learning; Small object detection; Deep learning; Small object detection; Computer vision; Convolutional neural networks
• ISSN: 0262-8856; 1872-8138
• DOI: 10.1016/j.imavis.2020.103910

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.