Local Flaw Detection with Adaptive Pyramid Image Fusion Across Spatial Sampling Resolution for SWRs

TL;DR

This paper introduces an adaptive pyramid image fusion method that improves local flaw detection in Steel Wire Ropes using Magnetic Flux Leakage imaging, effectively handling variations in inspection speed and sampling rate to enhance accuracy and robustness.

Contribution

The paper proposes a novel adaptive SSR target-feature-oriented method with pyramid image fusion to improve flaw detection under varying sampling resolutions.

Findings

Achieves over 94% precision and recall in high SSR scenarios.

Maintains high detection performance under low SSR conditions.

Outperforms conventional methods in accuracy and robustness.

Abstract

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.