Department of Civil Engineering, National Chung Hsing University, Taichung 40227, Taiwan.
Sensors (Basel). 2023 Feb 25;23(5):2572. doi: 10.3390/s23052572.
Bridges are often at risk due to the effects of natural disasters, such as earthquakes and typhoons. Bridge inspection assessments normally focus on cracks. However, numerous concrete structures with cracked surfaces are highly elevated or over water, and is not easily accessible to a bridge inspector. Furthermore, poor lighting under bridges and a complex visual background can hinder inspectors in their identification and measurement of cracks. In this study, cracks on bridge surfaces were photographed using a UAV-mounted camera. A YOLOv4 deep learning model was used to train a model for identifying cracks; the model was then employed in object detection. To perform the quantitative crack test, the images with identified cracks were first converted to grayscale images and then to binary images the using local thresholding method. Next, the two edge detection methods, Canny and morphological edge detectors were applied to the binary images to extract the edges of the cracks and obtain two types of crack edge images. Then, two scale methods, the planar marker method, and the total station measurement method, were used to calculate the actual size of the crack edge image. The results indicated that the model had an accuracy of 92%, with width measurements as precise as 0.22 mm. The proposed approach can thus enable bridge inspections and obtain objective and quantitative data.
桥梁经常由于地震、台风等自然灾害的影响而面临风险。桥梁检查评估通常集中在裂缝上。然而，许多表面有裂缝的混凝土结构位置很高或在水面上，对桥梁检查员来说不容易接近。此外，桥下光线不好，复杂的视觉背景会阻碍检查员识别和测量裂缝。在这项研究中，使用安装在无人机上的相机拍摄了桥梁表面的裂缝照片。使用 YOLOv4 深度学习模型训练了一个用于识别裂缝的模型；然后将该模型用于目标检测。为了进行定量裂缝测试，首先将带有识别出的裂缝的图像转换为灰度图像，然后使用局部阈值法将其转换为二进制图像。接下来，应用两种边缘检测方法，即 Canny 和形态边缘检测器，对二进制图像进行处理，以提取裂缝的边缘，并获得两种类型的裂缝边缘图像。然后，使用两种比例尺方法，即平面标记法和全站仪测量法，计算裂缝边缘图像的实际尺寸。结果表明，该模型的准确率为 92%，宽度测量精度高达 0.22 毫米。因此，该方法可以实现桥梁检查，并获得客观和定量的数据。
