针对隐蔽微缺陷检测难题，提出上下文感知的实时检测框架。
CF-YOLO: Context-Aware Feature Refinement for Camouflaged Industrial Micro-Defect Detection
- 融合大核感知与小核聚合，突破背景伪装限制
- 线性复杂度特征混合器提升微缺陷表征精度，误检率降低3.9%
- 适用于高精度工业质检，尤其适合复杂背景下的微小缺陷检测
工业部件表面微缺陷（如铜管）的自动化检测对质量控制至关重要，但因异常尺度极小且易被复杂背景遮蔽，导致特征表达弱、误检和漏检率高。为此，提出一种面向高效上下文感知与特征精炼的实时检测框架。方法集成上下文感知聚合模块（CPAM），协同运用大核感知宏观纹理上下文与小核聚合实现锐利边界刻画，有效打破背景伪装。同时，引入特征加性精炼模块（FARM），采用线性复杂度加性令牌混合器，全局验证并优化细粒度异常表征，抑制噪声干扰。为支持该领域研究，构建了人工标注的铜管缺陷数据集（CTDD），包含1,847张图像和4,898个边界框缺陷实例。大量实验表明，本检测器在CTDD上性能显著优于代表性基线模型，相比YOLOv11提升mAP@50 2.2%、Precision 3.9%，同时保持实时推理速度。该工作为高精度工业检测提供了鲁棒高效的解决方案，弥合了上下文理解与细节分析之间的差距。代码与模型已公开于：https://github.com/Yu-Xinda/CFYOLO-Context-Aware-Feature-Refinement-for-Camouflaged-Industrial-Micro-Defect-Detection
原文摘要 · Abstract (English)
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
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