With the continuous development of society, with the advent of the era of big data, situational awareness systems are gradually becoming well known and play an important role. Situational awareness systems are based on safe big data, and they are environmentally, dynamically, and holistically aware of security. A comprehensive system of risk capabilities. Therefore, this article uses the situational awareness system to study the rescue problem of mine fires, in order to reduce the casualties and economic losses caused by mine fires. On this basis, the convolutional neural network algorithm is used for situational awareness. By optimizing the algorithm, from region-based convolutional neural network (R-CNN) model to fast R-CNN model, the optimal model of faster R-CNN is finally proposed and implemented. The mine fire rescue problem.

中文翻译：

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基于Faster Ross Girshick-卷积神经网络的矿井火灾救援态势感知系统研究

随着社会的不断发展，随着大数据时代的到来，态势感知系统逐渐为人们所熟知并发挥着重要作用。态势感知系统基于安全的大数据，它们具有环境、动态和整体安全意识。全面的风险能力体系。因此，本文利用态势感知系统对矿井火灾救援问题进行研究，以减少矿井火灾造成的人员伤亡和经济损失。在此基础上，采用卷积神经网络算法进行态势感知。通过优化算法，从基于区域的卷积神经网络（R-CNN）模型到fast R-CNN模型，最终提出并实现了faster R-CNN的最优模型。矿井消防救援问题。