Concrete crack identification and detection method based on improved CNN and DoG operator

Haohua Luo; Yulun Wu; Yaoyang Liang; Jinshuai Ren; Zhiming Wang; Yilu Huang

doi:10.1117/12.3014461

9 January 2024 Concrete crack identification and detection method based on improved CNN and DoG operator

Haohua Luo, Yulun Wu, Yaoyang Liang, Jinshuai Ren, Zhiming Wang, Yilu Huang

Proceedings Volume 12969, International Conference on Algorithm, Imaging Processing, and Machine Vision (AIPMV 2023); 1296925 (2024) https://doi.org/10.1117/12.3014461
Event: International Conference on Algorithm, Imaging Processing and Machine Vision (AIPMV 2023), 2023, Qingdao, China

Abstract

Concrete will produce cracks under the long-term action of loads and affect the building safety, due to the unsatisfactory accuracy and efficiency of manual detection of concrete cracks, a concrete crack recognition and detection method based on improved CNN and DoG operator is proposed. Firstly, the recognition ability is trained by CNN to locate the valuable images from the image dataset, and then the locating crack images are greyscaled, denoised using bilateral filtering method, considering that the filtering will make the image edges blurred, the DoG operator is used to detect the completeness of the edges, and then the image is binary transformed by selecting thresholds through the one-dimensional Otus segmentation method, and the binary map is opened by the open operation, to fill in the broken parts within the cracks and protect the crack edges, and finally the length, width, and rotation angle of the crack are calculated by mapping the complete straight lines present in the crack through Hough space. The experimental results show that the method can accurately identify and detect crack features of different shapes with superior detection accuracy.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Haohua Luo, Yulun Wu, Yaoyang Liang, Jinshuai Ren, Zhiming Wang, and Yilu Huang "Concrete crack identification and detection method based on improved CNN and DoG operator", Proc. SPIE 12969, International Conference on Algorithm, Imaging Processing, and Machine Vision (AIPMV 2023), 1296925 (9 January 2024); https://doi.org/10.1117/12.3014461

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