DLR-YOLO: A High-Accuracy Lightweight Object Detector for Complex Underground Coal Mine Environments

Object detection in underground coal mines is plagued by critical challenges, including low illumination, high dust-induced noise, extensive target scale variation, frequent occlusion, and fragmented target feature representation, which commonly result in severe missed detections and insufficient detection confidence. To tackle these bottlenecks, this study proposes DLR-YOLO, a high-performance lightweight object detector built upon the YOLOv11n baseline, with three core optimized modules. Specifically, a dynamic multi-scale global perception enhancement module (DMGPEM) is embedded in the backbone to realize adaptive multi-scale feature extraction under low-light conditions; a lightweight cross-attention (LCA) module is integrated into the neck to achieve efficient fusion of shallow detail features and deep semantic features while suppressing dust-related noise; and a Reparameterized stem (RepStem) module is developed for initial feature extraction to minimize critical information loss during downsampling. Experimental results on our self-collected and annotated in-house underground coal mine dataset demonstrate that DLR-YOLO achieves 94.4% mAP@50 and 66.7% mAP@50-95, corresponding to 3.5 and 5.7 percentage point improvements over the YOLOv11n baseline, respectively. Ablation studies further validate the independent effectiveness of each proposed module. Meanwhile, the detector maintains a lightweight architecture with only 2.7M parameters and 6.6 GFLOPs, and reaches an inference speed of 157.1 FPS, outperforming several state-of-the-art real-time detectors, including YOLOv12, YOLOv13, and RT-DETR, on the same dataset. These findings confirm that DLR-YOLO provides a robust, high-performance technical foundation for real-time safety monitoring systems in complex underground coal mine environments.