基于视频图像的公路隧道火灾烟雾检测

为有效检测公路隧道火灾烟雾并预警，针对公路隧道传统火灾烟雾探测器存在的反应慢和功能单一等问题，通过分析研究火灾烟雾视频图像的颜色和纹理特征，提出一种基于烟雾图像特征的公路隧道火灾烟雾检测方法。首先，通过改进后的Vibe算法模型提取图像运动区域；然后，在YUV色彩空间中确定疑似烟雾区域后利用颜色滤出方法分割出疑似烟雾区域；最后，用从疑似烟雾区域图像中提取的颜色矩和均匀局部二进制模式（ULBP）与灰度共生矩阵（GLCM）构成机器学习分类器的输入向量进行隧道火灾烟雾识别。为满足复杂的隧道环境，对比分析BP神经网络、支持向量机、随机森林3种机器学习分类器的烟雾识别效果，选出最优算法作为公路隧道烟雾识别分类器。通过模拟公路隧道火灾烟雾试验视频和某实际公路隧道火灾视频对分类器进行试验测试，结果表明： 基于BP神经网络算法的检测系统识别性能最优，选取的烟雾特征具有较高识别精度，能够在隧道复杂环境中识别火灾烟雾。

Fire Smoke Detection in Highway Tunnels Based on Video Images

The detection of fire smoke in highway tunnels and its early warning are of great importance. Traditional fire smoke detectors have many disadvantages, such as slow response time and limited functionality. Accordingly, a fire smoke detection method based on image features associated with smoke is proposed, which can analyze the characteristics of color and the texture of fire smoke from video images. First, the area in an image associated with motion is extracted using the improved Vibe algorithm. Second, the suspected area corresponding to smoke is identified in YUV color space and segmented using a color filtering method. Finally, the color moments, uniform local binary mode, and gray level cooccurrence matrix are extracted from the image having the suspected area corresponding to smoke and are subsequently used to form the input vector of a machine learning classifier for recognizing fire smoke in highway tunnels. To meet the complex requirements of tunnel environments, the fire smoke recognition effects of three machine learning classifiers, i. e., back propagation (BP) neural network, support vector machine, and random forest algorithms, are compared and analyzed, and the optimal algorithm among them is selected as the smoke recognition classifier for highway tunnels. The classifier is tested using the simulated test videos of fire smoke in highway tunnels as well as the video of a real fire in a highway tunnel. The results show that the detection system based on BP neural network algorithm has the best recognition performance and the selected smoke features have high recognition accuracy for identifying fire smoke in complex highway tunnel environments.