Mine external fire monitoring method using the fusion of visible visual features
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摘要: 为了解决矿井外因火灾监测实时性差、误报率和漏报率高的问题，提出了基于可见光视觉特征融合的矿井外因火灾监测方法。首先，分析了不同监测环境中火源视频图像对应的视觉特征，并设计了火源纹理、尖角、相似系数和闪动频率的提取方法；其次，采用改进的种子区域生长算法对火灾疑似区域进行分割，并利用不同的火源特征提取方法计算火灾疑似区域的动、静态特征；然后，融合所提取的动、静态特征，构建火灾特征向量；最后，构建了基于BP神经网络的火灾监测模型，并对监测模型进行了验证。结果表明，本文提出的火灾监测方法可有效检测不同场景和不同距离下的外因火灾，正确率和检测率分别达到98.60%和99.09%，误检率低至2.00%，并有很强的抗干扰能力和鲁棒性。Abstract: In order to overcome the problems of poor real-time performance, high false alarm rate and underreport alarm rate of mine external fire monitoring, a method of fire monitoring using the fusion of visible visual features is proposed.Firstly, the visual features corresponding to the video images of fire sources in different monitoring environments are analyzed, and the extraction methods of fire source texture, sharp corners, similarity coefficient and flicker frequency are designed.Then, an improved seed region growth algorithm is used to segment the suspected fire area, and different feature extraction methods are used to calculate the dynamic and static characteristics of the suspected fire area.Secondly, the extracted dynamic and static features are used to construct fire feature vectors.Finally, a fire monitoring model using BP neural network is constructed, and monitoring model is verified.The results show that the proposed fire monitoring method can effectively detect mine external fire in different scenes and distances.The correct rate and detection rate are 98.60% and 99.06%, respectively, the false detection rate is as low as 2.00%.It has strong anti-interference ability and robustness.
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Key words:
- external fire /
- fire monitoring /
- static features /
- dynamic features /
- feature fusion /
- BP neural networks
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图 5 不同算法的评价指标直方图
Figure 5. Histograms of evaluation indicators for different algorithms
表 1 不同监控场景的视频数据信息
Table 1. Video data information of different monitoring scenarios
编号 目标类型 视频场景描述 视频/帧 1 火灾 早期点状火源，用于检测早期阶段的火灾 3 000 2 火灾 早期蔓延火源1，用于检测蔓延过程中的火灾 3 000 3 火灾 早期蔓延火源2，用于检测蔓延过程中的火灾 3 000 4 火灾 早期蔓延火源3，用于检测蔓延过程中的火灾 3 000 5 火灾 远距离点状火源，用于检测监视距离较远的火灾 3 000 6 干扰源 固定的巷道灯，用于排除井下固定的干扰光源 3 000 7 干扰源 矿工佩戴的矿灯，用于排除井下慢变的干扰光源 3 000 8 干扰源 设备的信号灯，用于排除井下闪烁的干扰光源 3 000 9 干扰源 正常行进的车灯，用于排除井下快变的干扰光源 3 000 10 干扰源 设备表面反射的光斑，用于排除井下的反射光源 3 000 
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表 2 不同算法的ACC指标值
Table 2. ACC values of different algorithms
算法 表 1中视频场景 1 2 3 4 5 6 7 8 9 10 Static & BP 55.26 81.39 59.26 68.94 51.54 94.42 48.84 89.21 92.05 93.14 Dynamic & BP 39.47 63.57 49.07 60.61 55.38 85.16 80.72 69.06 72.58 82.09 文献[22] 28.53 59.62 37.12 48.79 31.55 85.01 72.09 89.56 90.20 87.59 文献[23] 15.79 55.81 30.56 40.91 23.85 97.22 81.00 90.24 87.80 95.65 文献[10] 81.58 91.47 94.44 87.04 85.19 53.05 37.79 91.30 80.21 85.62 本文算法 57.89 71.67 65.37 62.88 42.31 99.01 82.78 92.81 93.42 97.86
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