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

Small object detection is a challenging problemin computer vision. It has beenwidely applied in defensemilitary, 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. (C) 2020 Elsevier B.V. All rights reserved.