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A Review of Computer Vision-Based Damage Detection in Steel Structures
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Yikang LIU1, 2, Mingxuan ZHANG1, 2, Qianqian YU1, 3
Industrial Construction | 2026, 56(5) : 215 - 231
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Industrial Construction | 2026, 56(5): 215-231
A Review of Computer Vision-Based Damage Detection in Steel Structures
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Yikang LIU1, 2, Mingxuan ZHANG1, 2, Qianqian YU1, 3
Affiliations
  • 1Department of Structural Engineering， Tongji University， Shanghai200092， China
  • 2Key Laboratory of Performance Evolution and Control for Engineering Structures， Tongji University， Shanghai200092， China
  • 3State Key Laboratory of Disaster Reduction in Civil Engineering， Tongji University， Shanghai200092， China
Published: 2026-05-20 doi: 10.3724/j.gyjzG26033109
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Efficient and reliable structural health monitoring is essential for ensuring the safety and extending the service life of steel structures. Owing to the advantages of non-contact nature， high efficiency， and a high degree of automation， computer vision （CV） has gradually become an important technology for the inspection and maintenance of steel structures. Focusing on surface cracks and corrosion damage of steel structures， this review systematically summarizes the recent research progress in CV-based damage detection and outlines the major approaches， including image classification， object detection， and image segmentation. Particular attention is paid to key optimization strategies for small object detection， robustness under complex backgrounds， few-shot learning， and on-site deployment. Existing studies indicate that CV has significantly improved the automation， intelligence， and precision of damage detection for steel structures. However， further advances are still required in dataset standardization， model robustness to interference， generalization capability across scenarios， and lightweight real-time inference.

steel structure  /  damage detection  /  computer vision  /  deep learning
Yikang LIU, Mingxuan ZHANG, Qianqian YU. A Review of Computer Vision-Based Damage Detection in Steel Structures[J]. Industrial Construction, 2026 , 56 (5) : 215 -231 . DOI: 10.3724/j.gyjzG26033109
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Year 2026 volume 56 Issue 5
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Article Info
doi: 10.3724/j.gyjzG26033109
  • Receive Date：2026-03-31
  • Online Date：2026-06-25
  • Published：2026-05-20
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  • Received：2026-03-31
Affiliations
    1Department of Structural Engineering， Tongji University， Shanghai200092， China
    2Key Laboratory of Performance Evolution and Control for Engineering Structures， Tongji University， Shanghai200092， China
    3State Key Laboratory of Disaster Reduction in Civil Engineering， Tongji University， Shanghai200092， China
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