Building crack identification and total quality management method based on deep learning.

• A crack data set with various forms is constructed by manual collection. • Proposes a network model suitable for multi shape object segmentation. • Proposes a kind of boundary weighted loss function which is suitable for the segmentation network of small objects. • Our model has better detection effect than the existing crack segmentation network in this scene. The existence of cracks will affect the stability of the building. It is very important to identify and deal with the cracks in time to ensure the safety and stability of the building. Based on the above background, the purpose of this paper is to study the method of building crack recognition and total quality management based on deep learning. This paper focuses on the computer vision technology in artificial intelligence, studies the image classification algorithm and semantic segmentation algorithm based on the deep learning method, and applies it to the field of building crack image analysis. In this paper, we use the deep convolution neural network to design the building image crack classification model and segmentation model, realize the identification and analysis of building cracks, and build a building crack analysis system, which can significantly improve the efficiency of building crack detection. Then, based on the image processing technology, the quantitative analysis of the fracture segmentation results is carried out. Through the basic morphological methods such as corrosion, expansion, opening and closing operations, the segmentation mark map, skeleton map and geometric parameter information of the fracture are obtained, which further provides the maintenance and judgment basis for professional engineers. The experimental results show that compared with FCN, the accuracy of rfcn-a is improved by 5.98%, the precision is improved by 6.07%, and the real and f'score are improved by 3.11% and 6.01%, respectively. [ABSTRACT FROM AUTHOR]