复杂场景下的道路交通标志识别研究OA

In view of the problems of low recognition accuracy and high missed detection rate of traffic signs un-der complex environmental conditions,an improved traffic sign recognition algorithm YOLO-Traffic based on YOLOv8(You Only Look Once version 8)is proposed.The multi-scale information extraction ability of the network is enhanced through scale sequence feature fusion and triple feature coding.The local fine-grained features of traffic signs are fully extracted by adding a small target detection layer and refining the local feature mapping.The CA(Co-ordinate Attention)attention mechanism is introduced into the backbone network to enhance the model's ability to fo-cus on key regions.The new metric NWD(Normalized Wasserstein Distance)is adopted to replace the CIoU(Com-plete Intersection over Union)in the regression loss function of the detection head,strengthening the detection ability for small targets.The experimental results show that the mAP@0.5(mean Average Precision)of the original model is 90.4%,the mAP@0.5:0.95 is 63.2%,and the model size is 6.3 MB.The mAP@0.5 of the improved model is 95.5%,the mAP@0.5:0.95 is 67.5%,and the model size is 5.2 MB.Compared with the original model,the vol-ume of the improved model is reduced by 17.5%.The improved algorithm reduces the volume of model parameters while enhancing detection accuracy,and can meet the requirements of various complex road conditions and light-weight in practical application scenarios.