Abstract Current concrete surface crack detection methods cannot simultaneously achieve high detection accuracy and e.ciency. Thus, this study focuses on the recognition and classi.cation of crack images and proposes a concrete crack detection method that integrates the Inception module and a quantum convolutional neural network. First, the features of concrete cracks are highlighted by image gray processing, morphological operations, and threshold segmentation, and then the image is quantum coded by angle coding to transform the classical image information into quantum image information. Then, quantum circuits are used to implement classical image convolution operations to improve the convergence speed of the model and enhance the image representation. Second, two image input paths are designed: one with a quantum convolutional layer and the other with a classical convolutional layer. Finally, comparative experiments are conducted using different parameters to determine the optimal concrete crack classi.cation parameter values for concrete crack image classification. Experimental results show that the method is suitable for crack classification in different scenarios, and training speed is greatly improved compared with that of existing deep learning models. The two evaluation metrics, accuracy and recall, are considerably enhanced.
Fund: This work was supported by 2023 National College Students' Innovation and Entrepreneurship Training Program project "Building Crack Structure Safety Detection based on Quantum Convolutional Neural Network intelligent Algorithm - A case study of Sanzhuang Town, Donggang District, Rizhao City" (NO.202310429224).
About author: Bu Yun-zhe (2003-), male, born in Zoucheng City, Jining City, Shandong Province, undergraduate student, studying civil engineering at Qingdao University of Technology.
Cite this article:
. Recognition and Classification of Concrete Surface Cracks with an Inception Quantum Convolutional Neural Network Algorithm[J]. APPLIED GEOPHYSICS, 2025, 22(4): 1475-1490.
2025, Vol. 22 
