Rock fractures significantly influence rock classification and engineering parameter determination. Traditional joint extraction methods are time-consuming, subjective, and error-prone, hindering intelligent tunnel engineering and compromising construction safety. This study proposes an automated rock integrity quantification method using the GAM-BiFPN-YOLOv8-seg model. A standardized process for collecting tunnel face images was established, creating a diverse joint image dataset. Incorporating global attention mechanisms and weighted bidirectional feature pyramid networks into the YOLOv8-seg framework improved detection of subtle joint features and multi-scale fusion, enhancing joint extraction accuracy. After binarization and applying the Zhang-Suen thinning algorithm, joint traces were combined with surrounding joint counts to automate rock integrity quantification. Integrating the relationship between surrounding rock joint counts and rock mass integrity indices, the method achieved a 6.34% improvement in mAP50 and an 8.42% improvement in mAP50:95 compared to the original YOLOv8-seg network, with marginal gains in detection speed. During engineering validation at the NEOM New City Tunnel project, the proposed method demonstrated consistent performance with human decision-making in quantifying tunnel face joints. The method provides an effective solution for the accurate assessment of rock mass integrity and has broad application prospects in the field of tunnel engineering and geological engineering. (1) To address inconsistent image acquisition standards for surrounding rock at tunnel faces, a standardized protocol was developed, resulting in a dedicated dataset for tunnel engineering applications. (2) Building on the YOLOv8-seg architecture, the novel GAM-BiFPN-YOLOv8-seg model was developed by integrating a GAM and a weighted BiFPN. (3) Based on the GAM-BiFPN-YOLOv8-seg model and the Zhang-Suen thinning algorithm, a relationship between joint counts and the rock integrity index is established to develop an automated method for quantifying rock integrity on tunnel faces. (4) The enhanced model demonstrates substantial improvements in accuracy, recall, F1 score, mAP50, and mAP50:95 metrics on a proprietary dataset relative to the original model. (5) The practical implementation of the NEOM New Town tunnel project served to confirm the reliability and feasibility of the GAM-BiFPN-YOLOv8-seg model within engineering applications.
Dong et al. (Fri,) studied this question.