Evaluation of AI-based Visual Crack Detection in Steel Bridges Using Probability of Detection

2026-08-18

AI Summary

本文提出一种基于检测概率曲线的新统计评估框架，以评估AI视觉方法在钢桥裂缝检测中的有效性，促进其在安全关键应用中的广泛应用。

Abstract

Bridge structures are regularly inspected for structural damage such as cracks and corrosion in order to ensure public safety and reduce maintenance costs. Much research has been done on automating this process using computer vision methods, which are often evaluated and compared using metrics such as intersection over union, mean average precision, etc. However, predicting the actual effectiveness of an inspection method within the field of structural engineering from these metrics remains challenging. To enable the systematic use of these increasingly popular methods in engineering practice, evaluating the performance of these methods in a way that is compatible with standard engineering approaches is therefore an urgent necessity. We present a new statistical evaluation framework to allow the comparison of computer vision methods with conventional visual inspection for crack detection in steel bridges. The framework is based on probability of detection curves and can account for the influence of image resolution. We apply this evaluation method to the real-world ``Cracks in Steel Bridges'' dataset, which contains annotated images of cracks in bridge structures. The quantification of the probability of detection and its uncertainty enables a practical assessment of the effect of automated methods for damage detection in structural reliability analyses. In turn, this enables the wide-spread use of automated (AI-based) damage detection in safety critical applications. This evaluation method provides evidence that the proposed computer vision approach approach is robust for the crack detection task and can have a high added value as an addition to conventional visual inspection methods.

Problem

• AI-based Visual Crack Detection
• Steel Bridges
• Probability of Detection
• Engineering Practice

Innovation

• probability of detection
• computer vision
• steel bridges
• crack detection
• automated damage detection