Bridge Cable Inspections Face Major Safety and Accuracy Challenges
Cable-stayed bridges are vital modern structures, but their key load-bearing components, the stay cables, are highly susceptible to environmental damage like sheath breakage and corrosion. Traditional inspection methods, relying on manual visual assessment or hazardous access via ladder trucks, are inefficient, inaccurate, and unsafe.
While unmanned aerial vehicles (UAVs) offer an alternative, their safe operating distance prevents close-up, 360-degree cable inspection. Cable-climbing robots have thus emerged as a promising solution, yet existing designs are often constrained by large self-weight, limited operational speed, complex installation procedures, and restricted adaptability.
This paper addresses these limitations by presenting a lightweight, intelligent robotic system with swift climbing capabilities and broad diameter adaptability. Furthermore, it developed a comprehensive computer vision-based framework to automatically identify cable defects and calculate their geometry, addressing a key need for practical and efficient cable inspections.
A Dual-Wheel Robot Built for Stability, Speed, and Adaptability
The robot is designed to overcome key field challenges, including cable vibrations, inclinations from 30° to 90°, and diameters ranging from 70 mm to 270 mm. Its core is a dual-wheel driving mechanism, where two servo motor-driven active wheels grip the cable from below, providing the friction needed for stable and rapid climbing at speeds up to 26 meters per minute (m/min). The lightweight construction, utilizing carbon fiber and plastic, ensures easy installation and adaptability.
For inspection, an image acquisition module with four radially mounted cameras provides 360-degree coverage of the cable surface. A displacement sensor triggers image capture at precise intervals and records their location for accurate defect mapping.
The subsequent defect recognition framework processes these images through a multi-stage pipeline. It begins with illumination compensation via histogram equalization to improve contrast. Edge extraction then uses Gaussian filtering and the Sobel operator to highlight potential defects. The Hough line detection algorithm identifies and removes sheath edges, while template matching is used to filter out common rain lines.
Finally, the remaining regions are classified as defects. Using connected component analysis and contour extraction, the framework automatically calculates key morphological characteristics of each defect, such as its area and length, providing quantifiable data for maintenance decisions.
Computer Vision Framework Enables Automatic Defect Recognition
The intelligent robotic system was rigorously validated through extensive laboratory and field tests, proving its effectiveness for real-world bridge cable inspection. In laboratory settings, the robot demonstrated stable climbing on cables with various inclinations and under simulated rainy conditions. An initial two-wheel design was redesigned into a more stable four-wheel configuration, enabling it to achieve a climbing speed of 26 m/minute without slippage.
Subsequent field tests on multiple large-span cable-stayed bridges were conducted under challenging weather, including light rain and wind. The robot operated autonomously and reliably, adapting to cable diameters from 160 mm to 235 mm and maintaining a practical speed of 15–18 m/minute to ensure clear image capture. It successfully inspected 68 cables on one bridge in just three days, far surpassing the efficiency of manual methods.
The core defect recognition framework was evaluated on 1892 captured images, accurately identifying critical defects such as peeling, scratches, and grooves, and precisely locating them along the cable length. Quantitative analysis on representative cables showed the system achieved an average precision of 92.37 %, demonstrating highly reliable identification. The study also provided valuable practical insights, noting that most scratches measured under 0.35 m in length, with a few extending up to 1 m. These were predominantly located near the bridge deck and tower, indicating that scratches can occur early in a bridge’s service life, underscoring the need for regular inspections.
While the system is a major advancement, a key limitation is its difficulty in distinguishing structural defects from non-critical, greasy dirt. Future work will therefore focus on integrating deep learning to improve defect classification and further enhance accuracy for maintenance crews.
Delivering Reliable, Automated Maintenance for Modern Bridges
In conclusion, this study presented an intelligent robotic system that automates the inspection of bridge stay cables, overcoming the inefficiency and safety risks of manual methods. The lightweight, cable-climbing robot adapts to various diameters and inclinations, climbing stably at speeds up to 26 m/minute.
Its integrated computer vision framework processes images to automatically identify defects like peeling and scratches while filtering out interference from rain lines and sheath edges. Validated through laboratory and field tests, the system achieved a 92.37 % average defect detection precision, demonstrating a lightweight, adaptable, and highly efficient solution for maintaining critical bridge infrastructure and ensuring long-term structural safety.
Journal Reference
Yang, Y., Zhang, Q., Ji, Y., & Gui, Z. (2025). An Intelligent Robotic System for Surface Defect Detection on Stay Cables: Mechanical Design and Defect Recognition Framework. Buildings, 15(21), 3907. DOI:10.3390/buildings15213907. https://www.mdpi.com/2075-5309/15/21/3907
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