Fabric defect detection based on improved YOLOv7 network
Aiming at the failure of traditional target detection methods to balance detection accuracy,prediction speed and lightweight deployment model to realize real-time defects detection,a lightweight detection model based on improved YOLOv7 network was proposed.Firstly,lightweight convolution Ghost conv was introduced into the backbone network to reduce the number of network parameters while ensuring the detection accuracy and improve the detection efficiency of fabric defects.Secondly,CBAM attention mechanism was added to suppress useless information and enhance feature extraction ability.Finally,a new measurement method α-SIoU was introduced to replace IoU in the regression loss function to accelerate the degree of freedom of the loss function and improve the accuracy of the network model.The experiment shows that the accuracy P of the detection model reaches 96.27%,the average precision mAP value is 83.84%,the model size is only 19.10 MB,which effectively balances the accuracy,real-time and lightweight deployment of defects detection.
NETL
NSTL
万方数据
