Traffic Light Detection and Recognition Method Based on YOLOv5s and AlexNet

Abstract

Traffic light detection and recognition technology are of great importance for the development of driverless systems and vehicle-assisted driving systems. Since the target detection algorithm has the problems of lower detection accuracy and fewer detection types, this paper adopts the idea of first detection and then classification and proposes a method based on YOLOv5s target detection and AlexNet image classification to detect and identify traffic lights. The method first detects the traffic light area using YOLOv5s, then extracts the area and performs image processing operations, and finally feeds the processed image to AlexNet for recognition judgment. With this method, the shortcomings of the single-target detection algorithm in terms of low recognition rate for small-target detection can be avoided. Since the homemade dataset contains more low-light images, the dataset is optimized using the ZeroDCE low-light enhancement algorithm, and the performance of the network model trained after optimization of the dataset can reach 99.46% AP (average precision), which is 0.07% higher than that before optimization, and the average accuracy on the traffic light recognition dataset can reach 87.75%. The experimental results show that the method has a high accuracy rate and can realize the recognition of many types of traffic lights, which can meet the requirements of traffic light detection on actual roads.