MSC-DeepLabV3+: A Segmentation Model for Slender Fabric Roll Seam Detection

The application of deep learning in fabric defect detection has become increasingly widespread. To address false positives and false negatives in fabric roll seam detection, and to improve automation efficiency and product quality, we propose the Multi-scale Context DeepLabV3+ (MSC-DeepLabV3+), a semantic segmentation network designed for fabric roll seam detection, based on DeepLabV3+. The model improvements include enhancing the backbone performance through optimization of the UIB-MobileNetV2 network; designing the Dynamic Atrous and Sliding-window Fusion (DASF) module to improve adaptability to multi-scale seam structures with dynamic dilation rates and a sliding-window mechanism; and utilizing the Progressive Low-level Feature Fusion (PLFF) module to progressively restore seam boundary details via shallow feature fusion. Additionally, an enhanced 3-SE attention mechanism is employed, replacing the direct concatenation operation. Experimental results show that MSC-DeepLabV3+ outperforms classical and recent segmentation models. Compared to DeepLabV3+ with an Xception backbone, MSC-DeepLabV3+ achieves a mean intersection over union (mIoU) of 92.30% and the boundary F-score (BF) of 92.54%, representing improvements of 3.04% and 3.14%, respectively. Moreover, the model complexity is significantly reduced, with the model parameters (params) decreasing to 3.44M and Frames Per Second (FPS) increasing from 101 to 273, demonstrating its potential for deployment in resource-constrained industrial scenarios.