Towards Robust Industrial Micro-Defect Detection: AContext-Aware and Feature-Refined Architecture forCamouflaged Anomalies

Automated detection of surface micro-defects on industrial components, such as copper tubes, is critically important for quality assurance but remains challenging due to the minute scale of anomalies and their visual camouflage against complex backgrounds. These factors lead to weak feature representations and high rates of false positives and missed detections. To address these issues, we propose a novel real-time detection framework designed for efficient context perception and feature refinement. Our method integrates a Context-Perception Aggregation Module (CPAM), which synergises large-kernel perception for macro-texture context and small-kernel aggregation for sharp boundary delineation, effectively breaking the background camouflage. Furthermore, a Feature Additive Refinement Module (FARM) employs a linear-complexity additive token mixer to globally verify and refine the representation of fine-grained anomalies, suppressing noise-induced errors. To support research in this domain, we introduce the Copper Tube Defect Dataset (CTDD), a large-scale, annotated benchmark. Extensive experiments demonstrate that our detector achieves state-of-the-art performance on CTDD, surpassing strong baselines like YOLOv11 by margins of 2.2% in mAP@50 and 3.9% in Precision, while maintaining real-time inference speed. This work provides a robust and efficient solution for high-precision industrial inspection, bridging the gap between contextual understanding and detailed feature analysis. Our code and model are available at: https://github.com/Yu-Xinda/CF-YOLO-A-Context-Aware-and-Feature-Refined-Architecture-for-Camouflaged-Anomalies