Abstract: Under the background of intelligent construction of coal mines, real-time monitoring of underground personnel in coal mines is of great significance for ensuring mine safety. However, the common detection models currently deployed underground are difficult to meet the needs of real-time monitoring due to their large parameters. At the same time, due to the complex underground environment of coal mines, personnel detection is prone to missed detections and false detections. Therefore, an underground personnel detection algorithm that integrates attention and lightweight networks is proposed. Firstly, to solve the problem that the model parameters are too large and difficult to deploy, the C2f module of the model backbone network is replaced by the lightweight module C2f_RepGhost. Secondly, in order to improve the detection accuracy of the model, the EMA attention mechanism is added to the backbone network. Then, in order to enhance the model's small target personnel detection ability, the DyHead dynamic detection head is introduced. Finally, the original loss function is replaced by Inner-CIoU to optimize the target positioning accuracy. Comparative experiments are carried out on the PASCAL VOC 2012 dataset and the self-built coal mine underground dataset. The results show that on the VOC 2012 dataset, the improved model has an accuracy increase of 1.3% and a recall increase of 1.2% compared with the original model. On the self-built data set, the improved model parameters were reduced by 29.6%, and the accuracy and recall rates were increased by 2.4% and 3.5% compared with the original model, reaching 95.3% and 90% respectively. The improved model not only reduced parameters but also improved the model's missed detection and false detection situations, and can meet the actual requirements of underground personnel detection in coal mines.

摘要： 在煤矿智能化建设背景下，煤矿井下人员实时监测对于保障矿井安全具有重要意义。然而，目前常见检测模型在井下部署时由于参数过大，难以满足实时监测的需求，同时由于煤矿井下环境复杂，人员检测存在漏检、误检等情况，为此，提出了一种融合注意力和轻量级网络的井下人员检测算法。首先，针对模型参数量过大导致难以部署的问题，采用重构轻量化模块C2f_RepGhost替换模型主干网络的C2f模块。其次，为了提高模型检测准确率，在主干网络中添加EMA注意力机制。然后，为了增强模型的小目标人员检测能力，引入DyHead动态检测头。最后，使用Inner-CIoU替换原损失函数，优化目标定位精度。并在PASCAL VOC 2012数据集和自建煤矿井下数据集上进行对比实验，结果表明：在VOC 2012数据集上，改进后的模型相较原模型准确率提升了1.3%，召回率提升了1.2%。在自建数据集上，改进后的模型参数减少了29.6%，准确率和召回率相较原模型分别提升了2.4%和3.5%，达到了95.3%和90%，改进后的模型在减少参数的同时改善了模型漏检、误检的情况，能够满足煤矿井下人员检测的实际要求。