基于轻量化YOLOv5的交通标志检测OACSTPCD

In order to improve the detection speed of road traffic signs,an improved model based on lightweight YOLOv5 was proposed.Firstly,Ghost convolution and depthwise convolution were used to build a new Bottleneck,which could reduce the amount of computation and parameters.Then the BiFPN structure was introduced,which could enhance the feature fusion ability.CIoU loss function was replaced by SIoU loss function,which focused on the angle information of ground true box and prediction one,so that it would improve the detection accuracy.Sec-ondly,the TT100K dataset was optimized,and 24 categories of traffic sign pictures and labels with more than 200 were screened out.Finally,the experiment achieved 84%accuracy,81.2%recall and 85.4%mAP@0.5.Com-pared with the original YOLOv5 model,the number of parameters was reduced by 29.0%,the amount of computation was reduced by 29.4%,but the mAP@0.5 was only reduced by 0.1 percentages,and the detection frame rate was improved by 34 frames/s.Using the improved model for detection,the detection speed could be sig-nificantly improved,could basically achieve the goal of compression model on the basis of maintaining the detection accuracy.