基于 YOLOv5 算法的交通标志识别技术研究

针对传统方式识别交通标志算法存在的检测精度较低的问题,提出了一种改进YOLOv5算法的交通标志识别方法.首先改进YOLOv5算法的损失函数,使用EIOU损失函数代替YOLOv5算法所使用的GIOU损失函数来优化训练模型,提高算法的精度,实现对目标更快速的识别;然后使用加权Cluster非极大值抑制(NMS)改进YOLOv5本身所使用的加权NMS算法,提高生成检测框的准确率.实验结果表明,改进后的YOLOv5算法在由长沙理工大学制作的CCTSDB交通标志数据集上训练的模型的mAP值达到了84.35％,比原始的YOLOv5算法提高了6.23％.所以改进YOLOv5算法在交通标志识别中有更高的精度,能够更好的应用到实践当中.

Research on traffic sign recognition technology based on YOLOv5 algorithm

Aiming at the low detection accuracy of traditional traffic sign recognition algorithms,a traffic sign recognition method with improved YOLOv5 algorithm is proposed. First,improve the loss function of the YOLOv5 algorithm,use the EIOU loss function instead of the GIOU loss function used by the YOLOv5 algorithm to optimize the training model,improve the accuracy of the algorithm, and achieve faster identification of the target,then use the weighted Cluster NMS to improve the YOLOv5 itself. The weighted NMS algorithm improves the accuracy of generating the detection frame. The experimental results show that the mAP value of the model trained on the CCTSDB traffic sign dataset produced by Changsha University of Science and Technology by the improved YOLOv5 algorithm reaches 84. 35%, which is 6. 23% higher than the original YOLOv5 algorithm. Therefore,the improved YOLOv5 algorithm has higher accuracy in traffic sign recognition and can be better applied to practice.