Vision-based automated cable tension monitoring using pixel tracking

Abstract

Traditional methods for monitoring cable tension rely on indirect measurements such as cable vibrations and often require specialized calibration. These approaches limit the efficiency, and non-contact capability of tension monitoring across various structures. This paper presents a vision-based framework for automated cable tension monitoring, which directly captures image data of internal steel strands. By leveraging advanced computer vision techniques — such as zero-shot segmentation, depth estimation, edge detection, and dense pixel tracking — critical geometric parameters are extracted and integrated into a kinematic-based model for tension estimation. A calibration-free method for estimating real-world pixel size, derived from the helical geometry of the strands, enables field deployment without the need for camera setup information. Experimental results show strong correlation with reference data, achieving a mean absolute error of 4.94% under elastic conditions. These findings pave the way for a promising alternative in vision-based structural health monitoring for prestressed structures.