Underground roadway support is a critical component for ensuring safety in mining operations. In recent years, with the rapid advancement of intelligent technologies, computer vision-based automatic rock bolt detection methods have emerged as a promising alternative to traditional manual inspection. However, the underground mining environment inherently suffers from severely insufficient lighting. Images captured on-site often exhibit problems such as low overall brightness, blurred local details, and severe color distortion. To address the problem, this study proposed a novel low-light image enhancement algorithm, PromptHDR. Based on Transformer architecture, the algorithm effectively suppresses color distortion caused by non-uniform illumination through a Lighting Extraction Module, while simultaneously introducing a Prompt block incorporating a Mamba mechanism to enhance the model’s contextual understanding of the roadway scene and its ability to preserve rock bolt details. Quantitative results demonstrate that the PromptHDR algorithm achieves Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity (SSIM) index scores of 24.19 dB and 0.839, respectively. Furthermore, the enhanced images exhibit more natural visual appearance, adequate brightness recovery, and well-preserved detailed information, establishing a reliable visual foundation for the accurate identification of rock bolts.

During production, copper mining could generate substantial waste rock that impacts land use and the environment. Advances in deep learning have enabled efficient, cost-effective intelligent sorting, where vision sensor performance critically determines sorting accuracy and efficiency. However, the sorting environment of copper mine waste rock is inherently complex, particularly within the conveyor belt section of the sorting machine, where insufficient and uneven lighting significantly impairs the performance of vision-based detection systems. To address the above challenges, a deep-learning-based copper mine waste rock detection algorithm under low-light environments is proposed. Firstly, an Illumination Adaptive Transformer (IAT) module is added as a preprocessing layer at the beginning of the Backbone to enhance the brightness of the images acquired by the vision sensor. Secondly, a Local Enhancement-Global Modulation (LEGM) module is integrated after the A2C2f and C3k2 modules in the Neck to enhance the detection accuracy. Finally, to further improve the model performance, MPDIoU is introduced to optimize the original loss function CIoU. As a result, the proposed algorithm achieved an mAP@0.5 of 0.957 and an mAP@0.5:0.95 of 0.689, outperforming advanced methods by 1.9% and 8.6%, respectively.

Reliable personnel detection in underground mines is essential for safe intelligent mining, but uneven illumination, dust occlusion, thermal interference, and cross-modal parallax can degrade RGB-T perception. We propose an Environment-Driven Fusion and Optimization Network (EnvFONet), which uses environmental degradation as a unified prior for feature representation, multimodal fusion, and training optimization. First, Dynamic Receptive Field Relaxation (DRFR) adaptively interpolates original and locally smoothed thermal features to balance fine-detail preservation and contextual robustness. Second, an Env-AdaIN-guided spatially gated fusion module applies environment-conditioned residual affine modulation and high-frequency pixel-level weighting to reduce cross-modal statistical drift and parallax-induced boundary artifacts. Third, Environment-Adaptive Relaxation Loss (EARL) adjusts regression supervision for difficult matched samples according to environmental degradation and localization quality. On the self-collected Anshan underground iron-ore mine dataset, EnvFONet achieves 96.8% mAP50, 63.9% mAP75, and 62.8% mAP50:95. On the public LLVIP benchmark, it achieves 94.4% mAP50 and 59.6% mAP50:95. These results show that environment-conditioned fusion and optimization improve RGB-T personnel detection robustness under the evaluated degraded conditions.