Small Object Detection Based on Deep Learning

With the improving of the intelligent driving awareness, object detection as an important part of intelligent driving, has now become a research hotspot in the world. In recent years, convolutional neural network (CNN) has attracted more and more attention in the field of computer vision. CNN has made a series of important breakthroughs in the field of object detection. This paper introduces the object detection method based on deep learning. This paper mainly introduces the detection algorithm based on regional suggestion and regression, and analyzes the advantages and disadvantages of the detection algorithm and detection performance from two aspects of accuracy and speed. Then, the disadvantages of these detection methods in detecting small objects and the difficulties in detecting small objects are analyzed. On this basis, the public data sets and evaluation criteria related to small object detection are introduced.