基于检测概率的钢桥AI视觉裂缝检测评估
Evaluation of AI-based Visual Crack Detection in Steel Bridges Using Probability of Detection
- Eindhoven University of Technology（埃因霍温理工大学）
- Eindhoven Artificial Intelligence Systems Institute（埃因霍温人工智能系统研究所）
机构由 AI 辅助整理，请以论文原文为准。
中文总结 AI 辅助
本研究提出基于检测概率曲线的统计评估框架，对比钢桥AI视觉裂缝检测与传统视觉检测，验证AI方法鲁棒性及应用价值，推动自动化损伤检测在安全关键领域的应用。
中文摘要 AI 辅助
为保障公共安全、降低维护成本，需定期对桥梁结构进行裂缝、腐蚀等结构损伤检测。已有大量研究采用计算机视觉方法自动化该过程，常用交并比、平均精度均值等指标评估对比，但从这些指标预测结构工程领域检测方法的实际效果仍具挑战性。为使这类日益流行的方法能在工程实践中系统应用，亟需以符合标准工程方法的方式评估其性能。本文提出一种新的统计评估框架，用于对比计算机视觉方法与传统钢桥裂缝视觉检测方法，该框架基于检测概率曲线，可考虑图像分辨率的影响。将该评估方法应用于真实世界的“Cracks in Steel Bridges”数据集，该数据集包含桥梁结构裂缝的标注图像。对检测概率及其不确定性的量化，可在结构可靠性分析中实际评估自动化损伤检测方法的效果，进而推动自动化（基于AI的）损伤检测在安全关键应用中的广泛使用。该评估方法证明，所提出的计算机视觉方法在裂缝检测任务中具有鲁棒性，作为传统视觉检测方法的补充可产生高附加价值。
英文摘要
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.
