An efficient lightweight network for silk fabric defect detection

To address the challenges of balancing detection accuracy and computational efficiency in silk fabric defect detection, this paper proposes a lightweight model GSS-YOLOv8, designed to reduce parameter complexity while enabling real-time detection capabilities. A three-stage optimization strategy is adopted to target key bottlenecks. Firstly, in the backbone network, GhostHGNetV2 replaces the original feature extractor to enhance the feature representation of multiscale fabric defects while reducing the number of parameters. Secondly, a Slim-Neck structure is introduced, where the C2f module is replaced with VoVGSCSP and standard convolutions are substituted with GSConv, effectively reducing computational costs without sacrificing accuracy. Finally, a Shared Detail-Enhanced Head (SDEH) is designed. By sharing the parameters of two detail-enhanced convolutions, this module enhances the ability to capture fine-grained defect features and reduces parameter redundancy. The ablation experiments further evaluate the individual contributions of the GhostHGNetV2 backbone, the Slim-Neck paradigm design, and the proposed SDEH module, verifying the effectiveness of each improvement component. Additionally, experiments conducted on both the self-built silk fabric dataset and the Tianchi Fabric Defect Dataset confirm the feasibility and strong generalization capability of the proposed GSS-YOLOv8 model. The experimental results illustrate that compared with YOLOv8n, the GSS-YOLOv8 improves precision by 5.1 percentage points to 85.9% and mean average precision (mAP@0.5) by 2.1 percentage points to 86.5% with 80.0% recall, while reducing parameters by 51.3% to 1.46M, GFLOPs by 45.7% to 4.4G, and model size to only 3.7 MB, which fully meets the real-time detection requirements for silk fabric defects in industrial settings.