发表机构
School of Information Engineering, Huzhou University; School of Computer Science and Engineering, Nanjing University of Science and Technology（湖州大学信息工程学院; 南京理工大学计算机科学与工程学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对工业微缺陷检测中伪装导致的误漏检问题，提出CF-YOLO框架，含CPAM与FARM模块，在CTDD数据集上较YOLOv11实现性能提升，兼具高精度与实时性。
AI 中文摘要
工业部件（如铜管）表面微缺陷的自动检测对质量保障至关重要，但由于异常尺寸极小且在复杂背景中视觉伪装，该任务仍具挑战性。这些因素导致特征表示薄弱，误检和漏检率较高。为解决这些问题，我们提出一种新型实时检测框架，旨在实现高效的上下文感知与特征细化。该方法集成了上下文感知聚合模块（CPAM），其结合大核感知以获取宏观纹理上下文、小核聚合以勾勒清晰边界，有效打破背景伪装；此外，特征加性细化模块（FARM）采用线性复杂度的加性令牌混合器，全局验证并细化细粒度异常的表示，抑制噪声引发的误差。为支撑该领域研究，我们引入铜管缺陷数据集（CTDD），这是一个人工标注的基准，包含来自铜管检测场景的1847张图像和4898个边界框缺陷实例。大量实验表明，所提检测器在CTDD上表现出强劲且稳定的性能，超越包括YOLOv11在内的代表性基线检测器，mAP@50提升2.2%，精确率提升3.9%，同时保持实时推理速度。本研究为高精度工业检测提供了稳健高效的解决方案，弥合了上下文理解与详细特征分析之间的差距，代码和模型可在此URL获取。
英文摘要
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 manually annotated benchmark containing 1,847 images and 4,898 boundingbox defect instances from copper-tube inspection scenarios. Extensive experiments demonstrate that our detector achieves strong and consistent performance on CTDD, outperforming representative baseline detectors, including YOLOv11, by 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/CFYOLO-Context-Aware-Feature-Refinement-for-Camouflaged-Industrial-Micro-Defect-Detection
