DASC-YOLO: An Attention Scale-aware Framework for Real-time Leather Defect Detection with Limited Samples

Surface defect detection in leather manufacturing faces challenges including multi-scale defects, scarce samples, and texture interference. This study proposes an optimized YOLOv11 framework integrating attention mechanisms, cross-scale feature fusion, and few-shot learning. The backbone network employs spatial-channel attention and feature redundancy reduction to enhance defect discrimination. A cross-scale attention mechanism adaptively fuses multi-resolution features for improved small defect detection. A ProtoNet module addresses sample scarcity while ensuring localization precision. Evaluations on industrial leather datasets and public benchmarks demonstrate the model’s effectiveness, achieving 80.8 mAP on leather defects and 78.1 on steel surfaces with 3.02M parameters and real-time inference 70.3 FPS . The framework outperforms conventional methods in accuracy and robustness, offering a practical solution for automated quality inspection in texture-rich industrial scenarios. Our source code is available at https: github.com zuoqiumama DASC-YOLO.git