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Open Access

ARTICLE

FD-YOLO: An Attention-Augmented Lightweight Network for Real-Time Industrial Fabric Defect Detection

Shaobo Kang, Mingzhi Yang*
School of Electrical Engineering and Automation, Xiamen University of Technology, Xiamen, 361024, China
* Corresponding Author: Mingzhi Yang. Email: email

Computers, Materials & Continua https://doi.org/10.32604/cmc.2025.071488

Received 06 August 2025; Accepted 25 September 2025; Published online 27 October 2025

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.

Keywords

Deep learning; YOLO; fabric defect inspection; multi-scale attention; lightweight head

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