Surface defect detection method of strip steel based on improved YOLOv8n
Aiming at the problems of strip steel surface defects with various types and inconspicuous features,which lead to leakage and misdetection,an improved YOLOv8n strip steel surface defect detection method is proposed.Firstly,to adapt to smaller size targets,a P2 detection layer is added to identify various types of defects and reduce leakage detection,as well as an efficient PConv detection head is designed to maintain the inference speed.Secondly,a fusion of the C2f module in the neck of YOLOv8n and the deformable convolutional DCNv2 is adopted to enhance the feature extraction capability of the model.Furthermore,a large dynamics selective module is introduced in the output layer of the backbone network LSKNet,to expand the sensory field of the model and improve the accuracy of target detection.Finally,the SIoU loss function is chosen to replace the CIoU loss function to enhance the network convergence effect,thus improving the recognition accuracy.The improved YOLOv8n method is tested on the CSU_STEEL dataset,and the experimental results show that the mAP@0.5 is improved by 8.6%the original model to 82.3%,and the volume only increases by 0.5 MB.The improved method has better detection results for strip surface defects,which can provide a reference significance for the research of the defect detection method of strip steel.
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