Traffic Sign Small Target Detection Algorithm Based on Improved YOLOv5
Aiming at the low detection accuracy of traffic sign small targets and dense targets of traffic signs,an improved YOLOv5s detection model is proposed.It adds ECA attention mechanism to enhance feature information extraction ability of traffic sign small target in Backbone network.Secondly,it adopts SPPCSPC structure to reduce information loss of traffic sign small target.Then,it re-uses BiFPN network to fuse multi-scale feature information to enhance the fusion perception ability.Finally,WIoU is used as the loss function of the model during training to reduce excessive interference of background and improve the accuracy of traffic sign detection.The experimental results show that the accuracy of the improved algorithm is 93.3%,and the mAP value is 92.7%,which is 2.2%and 1.7%higher than before,respectively.
traffic sign small targetYOLOv5sECA attention mechanismSPPCSPC moduleWIoU loss
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