PofoliaShared via Pofolia

Structures· 2026Q1

Linear blue-light optical inspection for fatigue crack detection in steel bridges

Hongbin Guo, Zhongqiu Fu, Xincheng Li, Bohai Ji

Short summary

A novel linear blue-light optical inspection method significantly improves fatigue crack detection in steel bridges, achieving 75.63% mIoU and 78.51% F1-score by enhancing crack contrast and stability in challenging low-light, reflective environments.

AI-generated from the title and abstract; the full text is not read.

Key points

  • Developed a linear blue-light optical inspection method for fatigue crack detection in steel bridges.
  • Blue light enhances crack scattering sensitivity and contrast against metallic surfaces while suppressing background reflections.
  • A feature enhancement strategy further improves segmentation robustness by mitigating metallic texture and corrosion interference.
  • Achieved an mIoU of 75.63% and an F1-score of 78.51%, surpassing other methods in experiments.

AI-generated from the title and abstract; the full text is not read.

Abstract

Fatigue cracking is a critical concern for the structural integrity of steel bridges, particularly in enclosed steel box girders characterized by dim illumination, reflective metallic surfaces, and complex background interference. Under such conditions, low crack-to-background contrast and unstable imaging quality severely limit the robustness of automated crack detection and feature extraction. To address this issue, this study develops a linear blue-light optical inspection approach for fatigue crack detection in steel bridges. Benefiting from the short-wavelength and directional illumination characteristics of blue light, the proposed method enhances scattering sensitivity to fine crack discontinuities, suppresses background reflection interference, and improves crack contrast against metallic surfaces. A systematic comparison of representative industrial illumination configurations demonstrates that linear blue illumination provides superior crack discriminability and imaging stability under complex surface conditions. Furthermore, to mitigate residual interference from metallic textures, corrosion regions, and uneven reflections, a tailored feature enhancement strategy is introduced to strengthen crack-aware feature representations and improve segmentation robustness. Experimental results show that the proposed approach achieves an mIoU of 75.63% and an F1-score of 78.51%, outperforming several representative methods. The results demonstrate that integrating illumination optimization with data-driven feature enhancement provides an effective framework for fatigue crack inspection in steel bridges under low-light environments.

The authors' abstract, as published at the source. Structures, 2026 · DOI ↗

TakeawaysPremium
Ask the paperFree account

Continue with a free account

Ask the paper: 3 free questions a day about this paper; save it, get its citation, new summaries every day for your field. Takeaways are Premium.

Continue free on the web

Sign in with Google or Apple; no card needed. You come back to this paper.

On your phone:

Field: Mechanics of Materials

Mechanics of MaterialsEngineering