Pixel-wise detection algorithm for crack structural reconstruction based on rock CT images

This paper describes a pixel-wise detection algorithm to reconstruct rock cracks using CT images. Through reviewing the shortcomings of previous studies, a pixel-level labeled dataset with 1 k CT images covering diverse rocks is first introduced. Then, following four discoveries about how to design crack detection models better, three novel modules are presented, including 1) an inverted residual block with three hierarchical residual-like mixed connected branches, 2) the attention-based upsampling method that simultaneously executes multi-layer feature fusion, and 3) a multi-representative vector classifier. By employing these novel components and exploring the network structure, two attention-based networks (CTRCrack-T/B) with skip connections are presented for crack detection. Compared with previous state-of-the-art models, the proposed approach exhibits the best performance over the rock CT image dataset, achieving 87.8% mean intersection over union at 46.3 frames per second (5122 sizes). And abundant experiments report the superiority of CTRCracks in crack detection for other structures.