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, 人员检测通常存在漏检、误检等情况。为此, 在YOLOv8的基础上, 提出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替换原模型主干网络的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。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PASCAL VOC 2012数据集上, 改进模型相较原模型精确率提升了1.3百分点, 召回率提升了1.2百分点; 在自建数据集上, 相较原模型, 改进模型参数减少了29.6%, 精确率和召回率分别提升了2.4和3.5百分点, 分别达到了95.3%和90%。改进模型在减少参数量的同时, 改善了原模型漏检、误检的情况, 能够满足煤矿井下人员检测的实际要求。

关键词: 井下人员检测, YOLOv8, 轻量级网络, 注意力机制, 检测头

Abstract:

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 for personnel detection in underground coal mines.

Key words: underground personnel detection, YOLOv8, lightweight network, attention mechanism, detecting head