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

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 nondestructive testing (NDT), 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 and axial compression of LF features. These distortions, along with increased interference, further complicate 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 LF detection under different SSR scenarios. Experimental results show that under high SSR scenarios, the method achieves a precision of 92.16% and a recall of 96.91%. 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 (SOTA) 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.