Abstract: Object detection is one of the core issues and most challenging problems in the computer vision research field. With the wide application of deep learning technology, the efficiency and accuracy of object detection have gradually improved, which have reached or even exceeded the resolution level of the human eye in some respects. However, due to the small coverage area, low resolution, and insignificant features of the small object in the image, the existing object detection methods are not ideal for the detection of small object. Therefore, many special methods have been created to enhance the detection effect of small object. Based on extensive literatures, this paper thoroughly analyzes the reasons for the difficulty of small object detection, and fully discussed the methods for improving the detection effect of small object from multiple aspects such as multi-scale, feature context information, anchor box settings, intersection over union matching strategy, non-maximum suppression, loss function, generative adversarial network, object detection network structure and so on.

Key words: deep learning, object detection, small object