基于YOLOV5L的交通信号灯识别研究

Research on Traffic Signal Recognition based on YOLOV5L

由于交通信号灯目标较小,识别难度较大,为了精准探测出实时的交通信号灯变化,提出使用YOLOV5L来解决交通信号灯识别问题,可实现对目标图片、实时监控下的交通信号灯进行识别。使用了Pytorch框架,可以提供精度较高的实时监控识别功能,最后在自制交通信号灯数据集上进行实验操作,实验得出的mAP@.50最终稳定在78.6%。YOLOV5L模型可以基本满足交通信号灯的检测和识别。

In order to accurately detect real-time changes in traffic signals,and traffic signals belong to small targets with high recognition difficulty,this paper proposed to use YOLOV5L to solve the problem of traffic signal recognition.This system can achieve recognition of traffic signals under target images and real-time monitoring.This system uses the Pytorch framework,which can provide high-precision real-time monitoring and recognition functions.Finally,experimental operations were conducted on a self-made traffic signal dataset,and the experimental results were obtained mAP@.50 Finally,it stabilized at 78.6%.The YOLOV5L model could basically meet the detection and recognition of traffic signals.