A progressive rope groove wear measurement method based on improved OTSU-HSI and hierarchical extraction

Rope groove wear on elevator traction sheaves poses significant safety risks, yet conventional detection methods suffer from low efficiency and limited robustness to environmental interference. This study proposes a progressive wear measurement method combining improved OTSU-HSI segmentation with hierarchical centerline extraction. Anti-interference performance is enhanced through an improved OTSU segmentation strategy incorporating HSI color space conversion, and a hierarchical centerline extraction framework is constructed to achieve precise detection. First, the improved OTSU-HSI segmentation strategy is adopted to address the interference of oil contamination, dust, and other contaminants on light stripe images. Under oil contamination interference, the edge gradient of light stripe segmentation is elevated from 1.4273 to 2.2165. The parameters configured in the algorithm demonstrate good robustness across varying illumination conditions. Second, coarse localization by skeleton thinning is combined with fine calibration using the Steger algorithm to resolve the poor real-time performance of centerline extraction in the traditional Steger algorithm. While maintaining sub-pixel extraction accuracy, the average centerline extraction time is reduced, and the extraction speed is 1.2 times higher than that of the traditional Steger algorithm. Finally, rope groove depth is calculated through point cloud analysis. Experimental results demonstrate that the proposed method exhibits strong anti-interference capability and achieves a balance between speed and accuracy in centerline extraction. The relative error of rope groove depth detection is controlled within ±0.24%, and the coefficient of variation is as low as 0.78%, providing reliable real-time processing capability for dynamic monitoring.