Object detection has been well studied based on the traditional manual features and deep learning algorithm. However

the research on small object detection has just begun in recent years and there are little research outcome available. Furthermore

most of the methods proposed are based on the traditional object detection algorithms with certain modifications so as to improve the accuracy of small object detection. Small object contains fewer pixels and has less features

and it is even harder to extract object features after down sampling. Hence

small object detection is a challenging task. Small object detection has a wide range of application requirements in the fields of automatic driving

remote sensing image detection

and criminal investigation. It has important practical value for the research of small object detection technology. In this paper

the existing research results of small object detection are summarized. Firstly

the existing algorithms are classified into one stage

two stages and multi-stages according to the number of stages for detection. The principles of RetinaNet、CornerNet-Lite、feature pyramid network (FPN) and other algorithms are described and compared. Secondly

this paper describes the application of small object detection technology in different fields

and summarizes the data sets such as MS COCO、PASCAL VOC、DOTA、KITTI and algorithm performance evaluation indicators. Finally

the challenges faced by small object detection are concluded

and the future research directions are prospected.