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.

小物体检测是计算机视觉中一个具有挑战性的问题。它已广泛应用于国防军事，交通运输，工业等领域。为了促进对小物体检测的深入了解，我们从深度学习的角度出发，从多尺度特征学习，五个方面，五个方面全面综述了现有的小物体检测方法。数据扩充，训练策略，基于上下文的检测和基于GAN的检测。然后，我们对流行的数据集（例如MS-COCO，PASCAL-VOC）上一些典型的小物体检测算法的性能进行了全面分析。最后，从五个角度指出了未来可能的研究方向：新兴的小物体检测数据集和基准，多任务联合学习和优化，信息传输，