MultiScaleCrackNet: A parallel multiscale deep CNN architecture for concrete crack classification

Abstract

Structural health monitoring plays a crucial role in assessing the condition of civil structures and ensuring their safety and maintenance. One important aspect of structural assessment is the detection and analysis of cracks, which can occur in various structures such as bridges, buildings, tunnels, dams, monuments, and roadways. Recent advancements in crack classification and segmentation techniques have utilized convolutional neural network (CNN) variations. In this paper, we propose an end-to-end learning approach utilizing a deep hierarchical CNN for crack region identification. The proposed architecture leverages high pass kernels in the early convolutional layers to accurately capture the crack patterns, complemented by learnable filters to facilitate adaptive learning. Furthermore, parallel channels within the network enable the learning of cracks at multiple scales, accommodating the varying severity and characteristics of cracks. The model is trained using data at different scales and levels, ensuring comprehensive learning from the lowest to the highest convolutional layers. To validate the effectiveness of our approach, we conducted experimental evaluations using end-to-end training on the Concrete Crack Images for Classification dataset, Asphalt Crack Dataset, and SDNET2018 dataset for crack classification. The results demonstrate the robustness and efficacy of the proposed methodology in accurately identifying crack regions across different datasets.