基于改进教师学生网络的隧道火灾检测

Tunnel fire detection based on improved student-teacher network

隧道空间狭小,封闭性高,当发生火灾时,火势会迅速蔓延,导致救援难度增大,严重危害人们的生命财产安全。现有隧道火灾检测方法精度低且数据集匮乏,针对上述问题,提出一种基于改进教师学生网络的隧道火灾检测方法。首先,通过无监督学习对没有火灾的样本进行训练从而检测火灾,可以弥补隧道火灾数据集匮乏的问题,同时采用相同结构的学生网络和教师网络组成整体网络结构,在用于知识蒸馏的残差块中加入注意力机制以减少重要信息损失,过滤无关信息,其次用Mish激活函数代替Relu激活函数以提高网络性能,最后引入SPD-Conv模块代替跨步卷积层和池化层以提高较小火灾区域的检测精度。实验结果表明:改进的教师学生网络在自制隧道火灾数据集的像素级AUC-ROC和图像级AUC-ROC分别达到0.93和0.82,与现有隧道火灾检测算法相比,该模型检测精度均高于其他模型,验证了该模型的有效性。

Fire incidents in tunnels present serious hazards due to their rapid spread in confined spaces,endangering lives and property while making rescue operations challenging.Current tunnel fire detection methods suffer from inaccuracies and sufficient data.To address the above problems,a tunnel fire detection method based on an improved student-teacher network was proposed.Firstly,the proposed method trained unsupervised learning on fire-free samples to detect fires,compensating for the lack of tunnel fire datasets.At the same time,the student network and the teacher network with the same structure were adopted to form the whole network structure,and an attention mechanism was added to residual blocks for knowledge distillation to reduce the loss of important information and filter irrelevant information.Secondly,a Mish activation function was employed to replace a Relu activation function to enhance network performance.Finally,the SPD-Conv module replaced the strided convolution and pooling layer to improve the detection accuracy in smaller fire areas.The experimental results demonstrated that the pixel-level AUC-ROC and image-level AUC-ROC of the improved student-teacher network in the self-made tunnel fire dataset reached 0.93 and 0.82,respectively.Compared with the current tunnel fire detection algorithms,the detection accuracy of the improved model was higher than other models,substantiating its effectiveness.