Surface Defect Detection Method for Hot-rolled Strip Steel Based on Improved YOLOv8
A object detection algorithm based on improved YOLOv8s is proposed to address the issues of low accuracy and low efficiency in surface defect detection of hot-rolled strip steel.Firstly,an SPPD module based on feature map secondary stitching and incorporating GAM is proposed,which enhances the model's multi-scale information fusion ability.Secondly,a feature extraction module DCN-block that integrates deformable convolution is proposed to increase the receptive field of the model and extract complete defect information.Finally,the C2f module in the feature fusion network is replaced with a BoT(bottleneck transformer)structure,and the multi-head self-attention mechanism in the Transformer is fused with convolution to enhance the model's global position information perception ability.The experimental results show that the proposed algorithm achieves mean average precision(mAP)of 80.5%on the NEU-DET dataset,which is five percentage points higher than the original YOLOv8 algorithm.At the same time,the detection speed reaches 83 frames per second,meeting the requirements of real-time detection.
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