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Building crack identification and total quality management method based on deep learning
- Source :
- Pattern Recognition Letters. 145:225-231
- Publication Year :
- 2021
- Publisher :
- Elsevier BV, 2021.
-
Abstract
- 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.
- Subjects :
- Contextual image classification
Computer science
business.industry
Deep learning
Stability (learning theory)
Image processing
02 engineering and technology
Image segmentation
computer.software_genre
01 natural sciences
Convolutional neural network
Artificial Intelligence
0103 physical sciences
Signal Processing
0202 electrical engineering, electronic engineering, information engineering
020201 artificial intelligence & image processing
Segmentation
Computer Vision and Pattern Recognition
Data mining
Artificial intelligence
010306 general physics
Closing (morphology)
business
computer
Software
Subjects
Details
- ISSN :
- 01678655
- Volume :
- 145
- Database :
- OpenAIRE
- Journal :
- Pattern Recognition Letters
- Accession number :
- edsair.doi...........b2c9d4169fee9d4984a681c6aa08608a