基于图像处理的复杂场景火焰识别与火灾判定方法

Flame recognition and fire determination for complex scenes based on image processing

为了提高复杂场景及不同火灾场景火焰识别的准确性,提出一种基于图像处理的复杂场景火焰识别与火灾判定方法。结合应用高斯低通滤波、基于HSI的分块同态滤波增强与基于Lab的K-means彩色图像分割算法实现火焰识别,并通过计算评估火焰的面积变化率和质心分散来判定是否发生火灾。结果表明,高斯低通滤波避免了“振铃”现象的产生,且峰值信噪比(PSNR)较巴特沃兹和理想低通滤波方法更高;基于HSI的分块同态滤波增强算法抑制了背景光亮度的影响,并与对比度受限的自适应直方图均衡化(CLAHE)和色彩恢复的多尺度视网膜(MSRCR)算法比较,达到了更高的对比度;基于Lab的K-means分割算法降低了光线及设备对分割结果的影响,与Otsu和区域生长方法相比,能够更准确提取不同场景火灾图像的火焰。通过对30张不同场景的火灾图像进行火焰识别测试,其平均识别准确率达96.66%,处理1张图像用时0.96 s。最后,以某餐厅和室内蔓延火灾火焰及蜡烛稳定燃烧火焰为例,判定当火焰面积变化率不低于0.22且质心分散不低于17.02时发生火灾,应及时触发火灾报警器,降低火灾事故的损失。

In order to improve the accuracy of flame recognition in different fire scenes,a flame identification and fire determination in complex scenes based on image processing was proposed.Combined with the application of Gaussian low-pass filtering(GLF),HSI-based block homogeneous filtering enhancement(HSI-BHF)and LAB-based K-means segmentation algorithm(LAB-Kmenas)to achieve flame recognition,it’s determined whether a fire has occurred by calculating the area change rate and centroid dispersion of the flame.The results show that the GLF avoids the production of the"bell"phenomenon,and the PSNR are higher than the Butterworth and the ideal low-pass filtering.The HSI-BHF can suppress the influence of background brightness,achieve higher contrast compared with CLAHE and MSRCR.LAB-Kmeans reduces the effects of light and equipment,compared with Otsu and regional growth,it can more accurately extract the flame of in different scene fire images.Through the flame recognition test of 30 fire images in different scenes,the average recognition accuracy is 96.66%,and the average recognition time of one image is 1.94 s.Finally,taking the spreading fire of a restaurant and the steady flame of a candle as an example,after recognizing the video sequence image,it’s determined that when the flame area change rate is not less than 0.22 and the centroid dispersion is not less than 17.02,the fire alarm should be triggered in time to reduce the loss of fire accidents.