DMC-Net: a lightweight network for real-time surface defect segmentation

In industrial applications, surface defect segmentation is a critical task. However, facing challenges such as diverse defect scales, low contrast between defects and background, high interclass similarity and real-time detection in defect inspection, we propose an efficient lightweight network, named DMC-Net, for real-time surface defect segmentation. The structural optimization of DMC-Net includes the following components: (1) depthwise separable convolution attention module, a lightweight and efficient feature extraction module for extracting multi-scale defect features. (2) Multi-scale feature enhancement module, providing long-range information capture and local information focusing to enhance defect localization capability. (3) Channel shuffle group convolution, enhancing feature interaction and information propagation while reducing the parameter quantity. Based on the experimental results, DMC-Net achieved an mIoU of 73.74% on the NEU-SEG dataset, while achieving an FPS of 211.7. This indicates that we have successfully reduced the complexity and computational cost of the model while improving performance, providing a feasible solution for industrial applications. The relevant code can be obtained at https://github.com/Michaelzyb/DMC-Net.git.