A high-precision measurement method for dynamic cable winding gaps in multi-layer winding winches based on machine vision

Abstract High-precision online detection of cable winding gaps in tethered balloon winches is critical to ensuring the safe and stable operation of tethered systems. To address perspective distortion caused by the cylindrical drum surface, spurious edge interference from cable twisted texture and insufficient measurement accuracy under dynamic multi-layer winding conditions, this paper proposes a vision-based cable gap measurement method combining multi-layer geometric modelling with local directional dispersion enhancement. A recursive projection model of wave-like curves for multi-layer cables is constructed, which compensates for perspective errors from curved-surface depth variations through layer-by-layer object-distance correction. A Devernay-LoDD sub-pixel edge detection algorithm is proposed, which jointly suppresses texture spurious edges using gradient peak confidence and geometric direction confidence. Combined with dynamic ROI localisation, exponentially weighted guided filtering (EWGF) and RANSAC line fitting, a complete measurement pipeline is established. A visual detection system is built for experimental verification. The results show that the standard deviation for static repeated measurements is less than 0.004 mm, with a maximum absolute error of 0.0159 mm. Under the strong texture condition of the second layer, the mean absolute error is reduced by 56.4% compared with the traditional Devernay algorithm. Three-layer dynamic winding tests yield an average relative error of approximately 5.09%, with absolute errors below 0.10 mm for most measurement points. The geometric model has a layer-by-layer recursive property and can be extended to more winding layers, meeting the demand for high-precision online detection of cable winding gaps in tethered balloon winches.