Intelligent tunneling has emerged as a critical frontier in modern infrastructure engineering, where automation is essential to improving construction efficiency, precision, and safety. Within the widely adopted drill-and-blast method, accurate blasthole detection remains a major challenge due to harsh tunnel conditions such as dust interference, irregular rock textures, and low-resolution imaging. To address these issues, we propose YOLO-BD, a deep learning–based detection framework that extends YOLOv11 with three targeted architectural enhancements: the SPDConv module for information-preserving down-sampling, the C3K2PPA attention mechanism for multiscale feature refinement, and the MBConv module for lightweight yet expressive feature representation. In addition, an improved WIoUᵥ3 loss function is introduced to enhance localization robustness under noisy and complex environmental conditions. Experimental evaluations on a custom tunnel blasthole data set show that YOLO-BD achieves 94. 14% precision, 82. 12% recall, 87. 28% mAP50, and 49. 31% mAP50: 95, outperforming its YOLOv11 backbone by 4. 35% and 2. 97% on the respective mAP metrics. Visualization analyses further confirm YOLO-BD’s superior localization accuracy and reduced false detections under degraded imaging conditions. Comprehensive experiments on public benchmarks, including PASCAL VOC and RSOD, validate the model’s strong generalization capability, with YOLO-BD consistently surpassing baseline models across all detection metrics. Ablation studies and Grad-CAM visualizations substantiate the effectiveness of each proposed module. Overall, YOLO-BD offers a robust, accurate, and deployable solution for real-time blasthole detection in intelligent tunneling systems, with strong potential for integration into autonomous robotics and broader industrial inspection applications.
Jin et al. (Tue,) studied this question.