通过自适应融合图像提升钢丝绳缺陷检测精度，尤其在高速低采样场景下表现优异。
Local Flaw Detection with Adaptive Pyramid Image Fusion Across Spatial Sampling Resolution for SWRs
- 根据检测速度与采样率动态调整，构建自适应金字塔图像融合方法
- 高采样率下精度达94.73%，召回率96.77%；低采样率下召回率仍达97.32%
- 适合工业现场检测条件多变、对鲁棒性要求高的场景
钢丝绳局部缺陷（LFs）检测对多个行业的安全可靠性至关重要。磁通泄漏（MFL）成像常用于无损检测，但其效果常受检测速度与采样率共同影响。本文研究了检测速度与采样率对图像质量的影响，发现二者变化会导致条纹噪声、缺陷特征轴向压缩及干扰增加，影响准确检测。为此，提出将检测速度与采样率的关系定义为空间采样分辨率（SSR），并设计自适应SSR目标特征导向（AS-TFO）方法，结合自适应调节与金字塔图像融合技术，在不同SSR条件下提升缺陷检测性能。实验表明，在高SSR场景下，该方法精度达94.73%，召回率96.77%；在低SSR场景下，精度94.30%，召回率97.32%。整体优于传统方法，达到当前最优水平，显著提升复杂检测条件下的检测准确性与鲁棒性，适用于检测参数波动大的工业环境。
原文摘要 · Abstract (English)
The inspection of local flaws (LFs) in Steel Wire Ropes (SWRs) is crucial for ensuring safety and reliability in various industries. Magnetic Flux Leakage (MFL) imaging is commonly used for non-destructive testing, but its effectiveness is often hindered by the combined effects of inspection speed and sampling rate. To address this issue, the impacts of inspection speed and sampling rate on image quality are studied, as variations in these factors can cause stripe noise, axial compression of defect features, and increased interference, complicating accurate detection. We define the relationship between inspection speed and sampling rate as spatial sampling resolution (SSR) and propose an adaptive SSR target-feature-oriented (AS-TFO) method. This method incorporates adaptive adjustment and pyramid image fusion techniques to enhance defect detection under different SSR scenarios. Experimental results show that under high SSR scenarios, the method achieves a precision of 94.73% and a recall of 96.77%. It remains robust under low SSR scenarios with a precision of 94.30% and recall of 97.32%. The overall results show that the proposed method outperforms conventional approaches, achieving state-of-the-art performance. This improvement in detection accuracy and robustness is particularly valuable for handling complex inspection conditions, where inspection speed and sampling rate can vary significantly, making detection more robust and reliable in industrial settings.
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