FDSC-YOLOv8: Advancements in Automated Crack Identification for Enhanced Safety in Underground Engineering

Abstract

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 convolutionalmodule is replaced with a deformable convolutionalmodule. Secondly, themodel'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.