Wang Jinchao, Liu Houcheng, Han Zengqiang, Wang Yiteng
Institute of Rock and Soil Mechanics, Chinese Academy of Sciences, Wuhan, 430071, Hubei, China.
State Key Laboratory of Geomechanics and Geotechnical Engineering, Wuhan, 430071, Hubei, China.
Sci Rep. 2023 Aug 2;13(1):12532. doi: 10.1038/s41598-023-39740-z.
It is of great scientific and practical value to use effective technical means to monitor and warn the structural damage of bridges in real time and for a long time. Traditional image recognition network models are often limited by the lack of on-site images. In order to solve the problem of automatic recognition and parameter acquisition in digital images of bridge structures in the absence of data information, this paper proposes an automatic identification method for bridge structure damage areas based on digital images, which effectively achieves contour carving and quantitative characterization of bridge structure damage areas. Firstly, the digital image features of the bridge structure damage area are defined. By making full use of the feature that the pixel value of the damaged area is obviously different from that of the surrounding image, an image pre-processing method of the structure damaged area that can effectively improve the quality of the field shot image is proposed. Then, an improved Ostu method is proposed to organically fuse the global and local threshold features of the image to achieve the damaged area contour carving of the bridge structure surface image. The scale of damage area, the proportion of damage area and the calculation rule of damage area orientation are constructed. The key inspection and characteristic parameter diagnosis of bridge structure damage area are realized. Finally, test and analysis are carried out in combination with an actual project case. The results show that the method proposed in this paper is feasible and stable, which can improve the damage area measurement accuracy of the current bridge structure. The method can provide more data support for the detection and maintenance of the bridge structure.
运用有效的技术手段对桥梁结构损伤进行实时、长期监测与预警具有重大的科学和实用价值。传统的图像识别网络模型常常受到现场图像缺乏的限制。为了解决在缺乏数据信息的情况下桥梁结构数字图像的自动识别和参数获取问题，本文提出了一种基于数字图像的桥梁结构损伤区域自动识别方法，有效实现了桥梁结构损伤区域的轮廓提取和定量表征。首先，定义了桥梁结构损伤区域的数字图像特征。充分利用损伤区域像素值与周围图像明显不同的特点，提出了一种能有效提高现场拍摄图像质量的结构损伤区域图像预处理方法。然后，提出了一种改进的Ostu方法，有机融合图像的全局和局部阈值特征，实现桥梁结构表面图像损伤区域轮廓提取。构建了损伤区域的尺度、损伤区域比例以及损伤区域方向的计算规则。实现了桥梁结构损伤区域的关键检测和特征参数诊断。最后，结合实际工程案例进行了试验与分析。结果表明，本文提出的方法可行且稳定，能够提高当前桥梁结构损伤区域的测量精度。该方法可为桥梁结构的检测与维护提供更多的数据支持。
