YOLOv11-Based Enhanced Framework for Robust Blasthole Detection in Intelligent Tunnel Environments

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 SPD_Conv module for information-preserving down-sampling, the C3K2_PPA attention mechanism for multiscale feature refinement, and the MB_Conv module for lightweight yet expressive feature representation. In addition, an improved WIoU_v3 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.