Accurate interpretation of pointer-type gauges in submarine cabins is critical for operational safety but remains a laborious task due to confined spaces, disorganized visual backgrounds, and poor lighting conditions that contribute to crew eye fatigue. To address these challenges, this study presents an automated gauge reading approach that integrates a YOLOv11-based detection model with a dedicated value reading algorithm, deployed on an optical-see-through head-mounted display (HMD). The system first detects gauge regions of interest (ROIs) using a fine-tuned YOLOv11 model, followed by dial and pointer recognition via image processing techniques to compute measurement values, which are then overlaid on the HMD for operator confirmation and recording. Experimental evaluations conducted in a real submarine cabin environment demonstrate that the proposed YORO method significantly outperforms manual recording. Specifically, it reduces average task completion time by 92.5% (from 48.13 s to 3.58 s), decreases reading angular error by 77% (from 1.01° to 0.23°), and substantially lowers user workload, with a NASA-TLX score of 11.27 compared to 72.44 for the manual method (p < 0.001). These results validate the system’s effectiveness in enhancing efficiency, accuracy, and user experience. The proposed approach offers a practical framework for developing autonomous inspection systems in constrained industrial environments.
Dong et al. (Wed,) studied this question.