Underground Personnel Detection Algorithm Combining Attention and Lightweight Network
摘要
In intelligent coal mine construction, the real-time monitoring of underground personnel in coal mines is important to ensure mine safety. However, several parameters are used for real-time monitoring, and commonly used detection models often struggle to meet these requirements when deployed underground. Moreover, given the complex environment of underground coal mines, issues such as missed or false detections frequently occur during personnel detection. To address these issues, this paper proposes a coal mine underground personnel detection algorithm based on YOLOv8 that integrates attention mechanisms and lightweight networks. First, to address deployment difficulties caused by excessive model parameters, the C2f module in the backbone network of the original model is replaced with a reconstructed lightweight module called C2f_RepGhost. Second, to enhance the detection accuracy of the model, an EMA attention mechanism is incorporated into the backbone network. Third, to strengthen the capability of the model to detect small-target personnel, the DyHead dynamic detection head is introduced. Finally, the original loss function is replaced with Inner-CIoU to optimize the target localization accuracy. Comparative experiments on the PASCAL VOC 2012 dataset and a self-built underground coal mine dataset are conducted. The results demonstrate that, compared to the original model, the improved model achieves a 1.3 percentage point increase in precision and a 1.2 percentage point increase in recall on the PASCAL VOC 2012 dataset. On the self-built dataset, the improved model reduces the parameters by 29.6% compared to the original model, and the precision and recall reach 95.3% and 90%, respectively, which are 2.4 and 3.5 percentage points, higher than those of the original model, respectively. The improved model not only reduces the number of parameters but also mitigates the issues of missed and false detections in the original model, meeting the practical requirements