基于增强可变形卷积的带钢表面缺陷检测网络OA

A novel network architecture for strip steel surface defect detection was proposed,which designed an enhanced deformable convolution module based on co-attention mechanism and integrated it into the backbone network as a plugin.By leveraging the co-attention mechanism,the shape of the convolution kernel was adaptively adjusted,which effectively captured irregular defects on the strip steel surface,and significantly improved the feature extraction capability of the backbone network.Experimental results on the NEU-DET dataset demonstrated that the proposed method achieved an average precision(mAP)of 81.6%.