LLE-YOLO: Adaptive Low-Light-Enhanced and Degradation-Aware Multi-Scale Attention Network for Miner Detection in Underground Coal Mines

Underground coal mine environments commonly suffer from insufficient illumination, high dust concentrations, and cluttered backgrounds, which substantially degrade the accuracy of conventional object detection algorithms. To address these issues, this paper proposes LLE-YOLO, a detection network built upon YOLOv11n. At the input stage, an Adaptive Low-Light Enhancement Module (ALEM) is introduced, which integrates Retinex decomposition, Contrast-Limited Adaptive Histogram Equalization (CLAHE), and brightness-dependent Gamma mapping to dynamically select the optimal enhancement strategy according to the global luminance. Furthermore, a Degradation-Aware Efficient Multi-Scale Attention (DEMA) module is proposed, which incorporates Contrast-Aware Modulation (CAM), an asymmetric dilated convolution group, and a Degradation-aware Spatial Gate (DSG) into the EMA channel-grouping and cross-spatial learning framework, thereby strengthening multi-scale personnel detection while keeping the parameter count tractable. On the publicly available DsDPM66 dataset, which covers 66 coal mine sites and 105,096 annotated images, LLE-YOLO achieves an mAP@0.5 of 83.7%, representing gains of 8.1 percentage points over YOLOv11n and 5.2 percentage points over the GCB-YOLOv11 baseline, while the recall increases from 71.2% to 78.2%. Under extremely dark scenarios (