TY  - EJOU
AU  - Wang, Rui 
AU  - Liu, Zhihui 
AU  - Liu, Hongdi 
AU  - Su, Baozhong 
AU  - Ma, Chuanyi 

TI  - FDSC-YOLOv8: Advancements in Automated Crack Identification for Enhanced Safety in Underground Engineering
T2  - Computer Modeling in Engineering \& Sciences

PY  - 2024
VL  - 140
IS  - 3
SN  - 1526-1506

AB  - In underground engineering, the detection of structural cracks on tunnel surfaces stands as a pivotal task in ensuring the health and reliability of tunnel structures. However, the dim and dusty environment inherent to underground engineering poses considerable challenges to crack segmentation. This paper proposes a crack segmentation algorithm termed as Focused Detection for Subsurface Cracks YOLOv8 (FDSC-YOLOv8) specifically designed for underground engineering structural surfaces. Firstly, to improve the extraction of multi-layer convolutional features, the fixed convolutional module is replaced with a deformable convolutional module. Secondly, the model’s receptive field is enhanced by introducing a multi-branch convolutional module, improving the extraction of shallow features for small targets. Next, the Dynamic Snake Convolution module is incorporated to enhance the extraction capability for slender and weak cracks. Finally, the Convolutional Block Attention Module (CBAM) module is employed to achieve better target determination. The FDSC-YOLOv8s algorithm’s mAP50 and mAP50-95 reach 96.5% and 66.4%, according to the testing data.
KW  - Crack segmentation; improved YOLOv8; engineering applications; feature extraction

DO  - 10.32604/cmes.2024.050806