The three-dimensional (3D) reconstruction technology based on computer vision has greatly facilitated damage inspection and assessment and construction monitoring of civil engineering. However, there are several problems in its implementation. A new method of 3D reconstruction is hereby proposed in this paper regarding the geometrical characteristics of pictures of buildings. An algorithm can normalize the 3D-reconstructed point cloud model so that its length, width, and height are parallel to the coordinate axis of the world coordinate system, and the absolute scale of the point cloud model can be obtained. Compared with the traditional one, this methodology can maintain better accuracy. This paper establishes the theoretical framework of the methodology, and steps for implementation are given by using digital image processing technology. After analyzing the results of a field experiment, it has been proven that this methodology can make the best use of the geometric features and improve the efficiency of the traditional reconstruction algorithm and therefore result in high accuracy.

This paper proposes a weakly-supervised structural surface crack detection algorithm that can detect the crack area in an image with low data labeling cost. The algorithm consists of a convolutional neural networks Vgg16-Crack for classification, an improved and optimized class activation map (CAM) algorithm for accurately reflecting the position and distribution of cracks in the image, and a method that combines superpixel segmentation algorithm simple linear iterative clustering (SLIC) with CAM for more accurate semantic segmentation of cracks. In addition, this paper uses Bayesian optimization algorithm to obtain the optimal parameter combination that maximizes the performance of the model. The test results show that the algorithm only requires image-level labeling, which can effectively reduce the labor and material consumption brought by pixel-level labeling while ensuring accuracy.