A Three-Stage Surface-Defect Detection Pipeline for Bridge Stay-Cable Sheaths

LIU Yujing; LI Jingfan; FAN Yi; ZHANG Bailin; ZHU Yaoyu; WEI Xiaochen; LIU Yufei

doi:10.3724/j.gyjzG25062002

Volume 55 Issue 7

Jul. 2025

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LIU Yujing, LI Jingfan, FAN Yi, ZHANG Bailin, ZHU Yaoyu, WEI Xiaochen, LIU Yufei. A Three-Stage Surface-Defect Detection Pipeline for Bridge Stay-Cable Sheaths[J]. INDUSTRIAL CONSTRUCTION, 2025, 55(7): 11-20. doi: 10.3724/j.gyjzG25062002

Citation:

LIU Yujing, LI Jingfan, FAN Yi, ZHANG Bailin, ZHU Yaoyu, WEI Xiaochen, LIU Yufei. A Three-Stage Surface-Defect Detection Pipeline for Bridge Stay-Cable Sheaths[J]. INDUSTRIAL CONSTRUCTION, 2025, 55(7): 11-20. doi: 10.3724/j.gyjzG25062002

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A Three-Stage Surface-Defect Detection Pipeline for Bridge Stay-Cable Sheaths

doi: 10.3724/j.gyjzG25062002

1. Jiangsu Expressway Engineering Maintenance Technology Corporation, Nanjing 210049, China;
2. Department of Civil Engineering, Tsinghua University, Beijing 100084, China;
3. Jiangsu Runyang Bridge Development Corporation, Nanjing 210001, China;
4. Jiangsu Taizhou Bridge Corporation, Taizhou 225321, China;
5. CCCC National Engineering Research Center of Long Highway Bridge Construction Corporation, Beijing 100120, China

Received Date: 2025-06-20
Available Online: 2025-09-12

Abstract

Abstract

Surface defects of stay-cable sheaths directly jeopardize the structural redundancy and service life of cable-supported bridges. However， large depth-of-field imaging， cluttered backgrounds， and considerable cable length fragment visual information still impairs the accuracy of automated inspection accuracy. To remedy the inadequate data preprocessing and absence of holistic analysis in the existing work， an intelligent three-stage inspection pipeline that integrates instance-segmentation-based background removal， image mosaicking， and YOLO-based validation was developed. First， a fine-tuned U²-Net performed pixel-level cropping on images captured by a cable-climbing robot， suppressing background noise while preserving sheath details. Next， an enhanced regional SIFT matcher conducted longitudinal unfolding and transversed stitching to generate a seamless panoramic view of the cable surface. Finally， a task-oriented， improved YOLOv5 detector—augmented with lightweight attention and loss-function refinements—verified the effectiveness of the workflow for sheath-defect recognition. Experiments on a self-built data set of 1500 original images showed that the proposed model raised the mAP@0.5 by 2.8% comparing to the baseline YOLOv5 and maintained real-time inference. The three-stage pipeline markedly enhanced stay-cable sheath defect detection precision and supplied high-quality data for subsequent three-dimensional localization and service-life evaluation.
- stay-cable sheath,
- defect detection,
- image stitching,
- instance segmentation,
- intelligent inspection pipeline

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References(20)

References

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