Real-time detection of traffic signs combining GhostNetv2 and YOLOv7
In view of the low detection rate and poor robustness of existing lightweight detection methods for traffic sign detection,an improved YOLOv7 lightweight traffic sign detection algorithm was proposed.In the backbone network,a second-generation ghost convolution kernel was introduced to replace the existing convolution kernel structure.In the feature fusion network,larger-sized feature maps from the backbone network were involved in information fusion.In the training stage,an efficient intersection over union ratio function with a focus mechanism was used to calculate the target box regression loss,so as to coordinate the gradient function to calculate the target classification loss.In the testing stage,the model was deployed in a lightweight manner through TensorRT.The experimental results show that the improved model accuracy is 9.29%higher than YOLOv7,and the detection speed reaches 42.16 m·s-1,which is suitable for deployment in low-power hardware for real-time traffic sign detection tasks.
traffic sign detectionlightweight networksecond-generation ghost convolutiondecoupled full connected attention(DFC)improved loss function
国家科技期刊平台
NSTL
万方数据
