Industrial surface defect detection is critical for ensuring product quality and manufacturing efficiency across steel, electronics, and semiconductor sectors. However, practical deployment remains challenging due to the diversity of defect types, scale variations, and complex background noise. To address these issues, we propose YOLO-DCF (YOLO with Dual Distillation and Context-Aware Fusion), a novel and lightweight detection framework built upon YOLO11. The Context-Guided Dynamic Fusion FPN decomposes global context into orthogonal directional components, enabling precise localization of fine-grained defects while suppressing background noise, the C3k2-Dilated Multiscale Contextual Residual module leverages hierarchical receptive fields with parallel multi-dilation design to capture both local textures and global dependencies, and the Dual Block-Channel Knowledge Distillation module enhances model compression via a self-distillation mechanism by decoupling spatial and semantic knowledge flows, preserving essential representations during lightweight deployment. These modules enhance detection precision while maintaining real-time inference capability. Extensive experiments on NEU-DET and PKU-Market-PCB datasets validate the effectiveness of YOLO-DCF, which achieves mAP50 scores of 79.3% and 96.5%, respectively, representing significant improvements of 1.6% and 0.8% over baseline methods. Notably, YOLO-DCF demonstrates stronger recall and robustness in detecting fine-grained and low-contrast defects, while maintaining competitive real-time inference capability despite the increased model complexity. This work offers a practical and deployable solution for industrial quality inspection, and sets a new direction for efficient, distribution-aware visual recognition in manufacturing contexts.
Xing et al. (Fri,) studied this question.