Comparative Study and Analysis of Different Target Detection Algorithms in Traffic Sign Detection

Abstract

The advancement of artificial intelligence in transportation has led to a burgeoning interest in the research of automatic identification technologies, particularly in the realm of traffic signs. It is an important pioneer technology of unmanned driving technology and has great theoretical value and application prospect. However, traffic sign detection is faced with the influence of complex weather factors such as rain, snow and fog, as well as the problem that the target is partially blocked and the size of the target is very small. Hence, selecting a target detection algorithm capable of swiftly and precisely identifying traffic sign categories is imperative. This paper compared various target detection algorithms, trained and tested YOLO v3, YOLO v4, SSD and other algorithms using the same traffic sign data set (30 classes), and finally concluded that the YOLO v4 network had the best effect, with a mAP value of 83.28% and a convergence interval of total loss between 3.5 and 4.