摘要: Object detection has always been one of the hot tasks in the computer vision community, whose goal is to locate the instances from the image and predict instances' category. In recent years, with the development of deep learning t... 展开 Object detection has always been one of the hot tasks in the computer vision community, whose goal is to locate the instances from the image and predict instances' category. In recent years, with the development of deep learning technology, both the accuracy and speed of object detection have made great progress. However, limited by the low resolution and little feature information of the small objects, detecting the small object is still facing many difficulties and attracting more and more researchers' attention. In this paper, we first introduce the mainstream object detection algorithms, and then detail the development of small object detection algorithms from the perspective of the data enhancement, context learning, adversarial learning, feature fusion, and other aspects. Also, we analyze the performance of these representative algorithms on the common datasets. Finally, we summarize the existing problems and prospect the possible future development direction in the small object detection research field. 收起