Wang Yutong, Kou Ziming, Han Cong, Qin Yuchen
School of Mechanical Engineering, Taiyuan University of Technology, Taiyuan 030024, China.
Shanxi Provincial Engineering Laboratory for Mine Fluid Control, Taiyuan 030024, China.
Sensors (Basel). 2024 Oct 29;24(21):6943. doi: 10.3390/s24216943.
Coal gangue identification is the primary step in coal flow initial screening, which mainly faces problems such as low identification efficiency, complex algorithms, and high hardware requirements. In response to the above, this article proposes a new "hardware friendly" coal gangue image recognition algorithm, RRBM-YOLO, which is combined with dark light enhancement. Specifically, coal gangue image samples were customized in two scenarios: normal lighting and simulated underground lighting with poor lighting conditions. The images were preprocessed using the dim light enhancement algorithm Retinexformer, with YOLOv8 as the backbone network. The lightweight module RepGhost, the repeated weighted bi-directional feature extraction module BiFPN, and the multi-dimensional attention mechanism MCA were integrated, and different datasets were replaced to enhance the adaptability of the model and improve its generalization ability. The findings from the experiment indicate that the precision of the proposed model is as high as 0.988, the mAP@0.5(%) value and mAP@0.5:0.95(%) values increased by 10.49% and 36.62% compared to the original YOLOv8 model, and the inference speed reached 8.1GFLOPS. This indicates that RRBM-YOLO can attain an optimal equilibrium between detection precision and inference velocity, with excellent accuracy, robustness, and industrial application potential.
煤矸石识别是煤流初筛的首要步骤，主要面临识别效率低、算法复杂、硬件要求高等问题。针对上述问题，本文提出了一种新的“硬件友好型”煤矸石图像识别算法RRBM-YOLO，并结合了暗光增强技术。具体而言，在正常光照和光照条件较差的模拟井下光照两种场景下定制了煤矸石图像样本。采用暗光增强算法Retinexformer对图像进行预处理，以YOLOv8作为骨干网络。集成了轻量级模块RepGhost、重复加权双向特征提取模块BiFPN和多维度注意力机制MCA，并更换不同数据集以增强模型的适应性，提高其泛化能力。实验结果表明，所提模型的精度高达0.988，mAP@0.5(%)值和mAP@0.5:0.95(%)值相较于原始YOLOv8模型分别提高了10.49%和36.62%，推理速度达到8.1GFLOPS。这表明RRBM-YOLO能够在检测精度和推理速度之间达到最优平衡，具有优异的准确性、鲁棒性和工业应用潜力。
