Refined Segmentation Network for Leather Surface Defect Detection Based on Improved U-Net
Leather defects with variable morphology and high local similarity are of difficulty in extracting features comprehensively and accurately.In this work,a refined surface defect segmentation method based on improved U-Net network is proposed.On the encoder side,a cascaded dilated convolution module is embedded to obtain the global features while preserving the detail information of the original image,and a feature fusion module is added to the jump connection to reduce local features loss caused by directly splicing of the high-level and low-level feature tensor;on the decoder side,a decoding module based on the channel attention mechanism,which can guide the network to adaptively focus on defective regions,is used to replace the original convolu-tional layer;to further integrate high-level information,a global average pooling module is embedded as the se-mantic guide to improve the discrimination capability of the network from similar defects at the decoding end.The experimental results conducted on a leather dataset containing 7 kinds of defects show that the proposed method achieves 99.17％,93.27％,98.39％,and 88.88％in PA,MPA,FWIoU,and MIoU,which is 0.28,2.78,0.53,and 4.03 percentage points better than that of U-Net.The qualitative and quantitative analysis results demonstrate that the algorithm proposed has remarkable ability to refine the segmentation in leather defect recognition.
万方数据
维普
