@article{Kang2026, 
author = {Shaobo Kang and Mingzhi Yang},
title = {FD-YOLO: An Attention-Augmented Lightweight Network for Real-Time Industrial Fabric Defect Detection},
year = {2026},
journal = {Computers, Materials & Continua},
volume = {86},
number = {2},
pages = {1-23},
keywords = {Deep learning, YOLO, fabric defect inspection, multi-scale attention, lightweight head},
url = {https://www.sciopen.com/article/10.32604/cmc.2025.071488},
doi = {10.32604/cmc.2025.071488},
abstract = {Fabric defect detection plays a vital role in ensuring textile quality. However, traditional manual inspection methods are often inefficient and inaccurate. To overcome these limitations, we propose FD-YOLO, an enhanced lightweight detection model based on the YOLOv11n framework. The proposed model introduces the Bi-level Routing Attention (BRAttention) mechanism to enhance defect feature extraction, enabling more detailed feature representation. It proposes Deep Progressive Cross-Scale Fusion Neck (DPCSFNeck) to better capture small-scale defects and incorporates a Multi-Scale Dilated Residual (MSDR) module to strengthen multi-scale feature representation. Furthermore, a Shared Detail-Enhanced Lightweight Head (SDELHead) is employed to reduce the risk of gradient explosion during training. Experimental results demonstrate that FD-YOLO achieves superior detection accuracy and Lightweight performance compared to the baseline YOLOv11n.}
}