Crack detection method for concrete surface based on feature fusion

Article type: Research Article

Authors: Hong, Cheng

Affiliations: School of Civil Engineering and Architecture, Nanchang Institute of Technology, Nanchang, Jiangxi, China | E-mail: 2016994594@nit.edu.cn

Correspondence: [*] Corresponding author: School of Civil Engineering and Architecture, Nanchang Institute of Technology, Nanchang, Jiangxi, China. E-mail: 2016994594@nit.edu.cn.

Abstract: In recent years, detection methods based on deep learning have received widespread attention in the field of concrete crack detection. In view of the shortcomings of traditional image detection methods, a concrete crack detection method based on feature fusion is proposed. The Fourier frequency domain processed image is used as the input of the deep learning neural network. The original time domain image and the frequency domain image are respectively input into two feature extraction modules to extract high-level features, and then the two features are fused to fully characterize the characteristics of the time domain and frequency domain, and finally the concrete crack detection results of the feature fusion are obtained. The performance of the proposed method is compared with VGG-16, AlexNet and DenseNet. Experiments show that the accuracy of the proposed method is higher than VGG-16, AlexNet and DenseNet. The proposed method has good results in concrete crack detection. To verify the generalization ability of the proposed model, the Concrete Crack Images for Classification data set was input into the proposed model for testing. The experimental results show that the proposed model has good generalization ability.

Keywords: Crack detection, feature fusion, deep learning

DOI: 10.3233/JCM-247578

Journal: Journal of Computational Methods in Sciences and Engineering, vol. 24, no. 4-5, pp. 3275-3286, 2024