基于差分卷积和深浅层特征融合的表面缺陷分割

Surface Defect Segmentation Based on Pixel Difference Convolution and Deep-Shallow Feature Fusion

针对弱纹理情况下缺陷特征不明显和小目标缺陷分割准确性较低的问题,提出基于差分卷积和深浅层特征融合的表面缺陷分割方法。首先,基于差分卷积设计特征增强模块,对特征图进行特征增强;其次,在特征融合网络中将深浅层特征进行融合,对浅层网络中的细节特征和深层网络中的语义信息进行有效融合;最后分别采用NEU-Seg和MT-Magnetic数据集对本文方法和现有的代表性方法进行综合对比分析。结果表明:本文研究方法在NEU-Seg和MT-Magnetic数据集上分别实现85.2%、83.3%的分割精度,优于现有的代表性语义分割算法,证明该法可有效提升弱纹理和弱小缺陷的分割准确度,显著提高缺陷分割算法的精度。

To address the issues of unapparent defect features and low segmentation accuracy for small target defects under weak texture conditions,this article proposes a surface defect segmentation algorithm based on differential convolution and the fusion of shallow and deep networks.Firstly,a feature enhancement module is designed using differential convolution to enhance the feature maps.Secondly,the shallow and deep features are fused in the feature fusion network,effectively integrating the detailed features from the shallow network with the semantic information from the deep network.Finally,a comprehensive comparative analysis is conducted on the proposed method and existing representative approaches using the NEU-Seg and MT-Magnetic datasets.Experimental results show that the proposed method achieves an average intersection over union(mIoU)of 85.2%and 83.3%on the NEU-Seg and MT-Magnetic datasets,respectively,surpassing the current representative semantic segmentation algorithms.This demonstrates that the proposed method effectively improves the segmentation accuracy of weak textures and small defects,thereby significantly enhancing the precision of defect segmentation.