Recording of bridge damage areas by 3D integration of multiple images and reduction of the variability in detected results
Recording of bridge damage areas by 3D integration of multiple images and reduction of the variability in detected results
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DOI：
10.1111/mice.12971
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发表时间：
2023-01
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通讯作者：
T. Yamane;Pang-jo Chun;J. Dang;R. Honda
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文献类型：
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作者：
T. Yamane;Pang-jo Chun;J. Dang;R. Honda
已经开发了机器学习模型来使用图像执行损伤检测以提高桥梁检测效率。然而，在仅使用图像的损伤检测中，不能记录损伤的3D坐标。此外，检测的准确性取决于图像的质量。本文提出了一种方法，将从多个图像中检测到的损伤集成并记录到3D模型中，使用深度学习从桥梁图像中检测损伤，并从运动中检测结构以识别拍摄位置。所提出的方法减少了图像之间的检测结果的变化，可以评估损坏的规模，或者相反，在没有损坏和检查遗漏的程度。所提出的方法已被应用到一个真实的桥梁，它已被证明，实际的损伤位置可以记录为一个三维模型。
Machine learning models have been developed to perform damage detection using images to improve bridge inspection efficiency. However, in damage detection using images alone, the 3D coordinates of the damage cannot be recorded. Furthermore, the accuracy of the detection depends on the quality of the images. This paper proposes a method to integrate and record the damage detected from multiple images into a 3D model using deep learning to detect the damage from bridge images and structure from motion to identify the shooting position. The proposed method reduces the variability of the detection results between images and can assess the scale of damage or, conversely, where there is no damage and the extent of inspection omissions. The proposed method has been applied to a real bridge, and it has been shown that the actual damage locations can be recorded as a 3D model.