雾天条件下基于机器视觉的森林火灾监测

基于机器视觉的森林火灾监测已成为森林火灾监测的一个重要发展方向。烟雾是森林火灾监测的重要指标。然而，诸如云雾和类似烟雾的诸多干扰物降低了火灾识别精度，为此提出了一种结合去云雾和烟雾检测的基于机器视觉的森林火灾监测方法。首先，提取视频中若干帧图像作为样本图像，采用基于Haze-Line的去雾算法对样本图像进行去雾处理。然后，利用基于Horn-Schunck光流法的烟雾检测算法进行烟雾检测，并利用最大类间方差法去除相邻2帧图像间像素质量差异对烟雾检测的影响。最后，利用扩散性分析进行火灾判断。仿真实验及对比分析结果表明，本文方法能够检测出烟雾区域随时间逐渐增加的趋势，从而有效地进行雾天条件下的森林火灾监测，具有更高的准确性和鲁棒性。

Forest fire monitoring based on machine vision in foggy weather

Forest fire monitoring based on machine vision has gradually become an important development direction in the field of forest fire monitoring. Smoke is an important indicator of forest fire monitoring. However, many disturbances such as clouds and similar smoke greatly reduce the accuracy of fire recognition. In response to this problem, this paper proposes a machine vision based forest fire monitoring method that combines dehazed and smoke detection. Firstly, several frame images in the appro- priate sample video are extracted as sample images, and the sample images are dehazed by the Haze-Line based dehazing algorithm. Secondly, the smoke detection method based on the Horn-Schunck optical flow method is used to detect the smoke. The maximum inter-class variance method is used to remove the influence of the pixel quality difference between two adjacent frames on the smoke detection. Finally, diffusion analysis is used to do fire monitoring. Results of simulation experiments and comparative analysis show that the proposed method can detect the trend that smoke area gradually increases with time, so as to monitor forest fire under foggy conditions effectively with higher accuracy and robustness.