Axial Motion Prior-Guided Robust Detection of Surface Defects in Hoist Wire Ropes

Reliable detection of surface defects in hoist wire ropes is essential for the safe operation of hoisting systems. Because of the stranded rope structure, early broken wires and local scars often appear as weak, small-scale features embedded in repetitive textures. Axial rope motion introduces directional blur during image acquisition, further weakening defect boundaries and increasing missed detections and localization errors. To address these challenges, we propose RMP-YOLOv8n, an axial motion prior-guided detector based on YOLOv8n. The method incorporates a rope motion prior-guided high-frequency enhancement module (RMP-HFEM), which uses axial edge and motion high-pass responses to guide shallow feature enhancement, together with a P2 branch that retains spatial detail for small defects. All models were trained exclusively on clear images from the training split, while paired axial motion-blur subsets generated exclusively from the held-out test images were used only for robustness evaluation. Experimental results on the wire-rope defect dataset show that, under severe axial motion blur, the mAP@50 of the clear-trained YOLOv8n decreases from 94.87 ± 0.12% to 48.31 ± 0.11%, whereas RMP-YOLOv8n retains 90.62 ± 0.08% mAP@50 and 87.28 ± 0.15% recall across three training seeds, demonstrating substantially stronger robustness to axial motion blur. The proposed method provides technical support for the stable deployment of online hoist wire-rope inspection systems under high-speed operation, motion blur, and weak small-defect features.