Department of Engineering Mechanics and Energy, University of Tsukuba, 1-1-1 Tennodai, Ibaraki, Tsukuba 305-8577, Japan.
Sensors (Basel). 2022 May 4;22(9):3502. doi: 10.3390/s22093502.
Bridge inspection plays a critical role in mitigating the safety risks associated with bridge deterioration and decay. CV (computer vision) technology can facilitate bridge inspection by accurately automating the structural recognition tasks, especially useful in UAV (unmanned aerial vehicles)-assisted bridge inspections. This study proposed a framework for the multilevel inspection of bridges based on CV technology, and provided verification using CNN (convolution neural network) models. Using a long-distance dataset, recognition of the bridge type was performed using the Resnet50 network. The dataset was built using internet image captures of 1200 images of arched bridges, cable-stayed bridges and suspension bridges, and the network was trained and evaluated. A classification accuracy of 96.29% was obtained. The YOLOv3 model was used to recognize bridge components in medium-distance bridge images. A dataset was created from 300 images of girders and piers collected from the internet, and image argumentation techniques and the tuning of model hyperparameters were investigated. A detection accuracy of 93.55% for the girders and 82.64% for the piers was obtained. For close-distance bridge images, segmentation and recognition of bridge components were investigated using the instance segmentation algorithm of the Mask-RCNN model. A dataset containing 800 images of girders and bearings was created, and annotated based on Yokohama City bridge inspection image records data. The trained model showed an accuracy of 90.8% for the bounding box and 87.17% for the segmentation. This study also contributed to research on bridge image acquisition, computer vision model comparison, hyperparameter tuning, and optimization techniques.
桥梁检查在减轻与桥梁劣化和衰变相关的安全风险方面起着至关重要的作用。计算机视觉 (CV) 技术可以通过准确地自动化结构识别任务来促进桥梁检查，这在无人机 (UAV) 辅助桥梁检查中特别有用。本研究提出了一种基于 CV 技术的桥梁多层次检查框架，并使用卷积神经网络 (CNN) 模型进行了验证。使用远距离数据集，使用 Resnet50 网络对桥梁类型进行识别。该数据集是使用互联网上捕获的 1200 张拱形桥、斜拉桥和悬索桥的图像构建的，对网络进行了训练和评估。获得了 96.29%的分类准确率。使用 YOLOv3 模型识别中距离桥梁图像中的桥梁组件。从互联网上收集的 300 张梁和桥墩图像创建了一个数据集，并研究了图像扩充技术和模型超参数的调整。对于梁，获得了 93.55%的检测准确率，对于桥墩，获得了 82.64%的检测准确率。对于近距离桥梁图像，使用 Mask-RCNN 模型的实例分割算法研究了桥梁组件的分割和识别。创建了一个包含 800 张梁和轴承图像的数据集，并根据横滨市桥梁检查图像记录数据进行了标注。训练后的模型在边界框方面的准确率为 90.8%，在分割方面的准确率为 87.17%。本研究还为桥梁图像采集、计算机视觉模型比较、超参数调整和优化技术的研究做出了贡献。
