A Survey of Small Object Detection Based on Deep Learning

Abstract

As a basic visual recognition problem in computer vision, object detection has made great progress based on traditional manual features and deep learning algorithms. However, researches on small object detection ha ve only begun to appear in recent years, which has become a hot and difficult point in the field and most of them are improved on the basis of existing object detection algorithms to enhance the detection accuracy. With the rapid development of deep learning, small object detection based on deep learning has made great progress, which has wide application requirements in the fields of automatic driving, remote sensing image detection, criminal investigation and other fields, so the research on small object detection has strong practical values. In this paper, the existing research on small target detection is reviewed in detail. Firstly, the existing algorithms are divided into one stage and two stages according to the number of detection stages, and then the characteristics of these algorithms are analyzed; Secondly, the small object detection datasets commonly used are introduced. Finally, the challenges of small object detection are summarized, and the future research directions are prospected.