使用日夜两用型红外摄像机进行火焰检测

Fire Detection with Day-night Infrared Camera

为了提高火灾监控的准确性和及时性,并最大限度地利用已有硬件设备,提出一种使用日夜两用型红外摄像机的火灾自动监控方法.根据红外图像RGB空间的色彩分布特点,设计视频类型判断算法,实现视频图像类型自动切换.通过红外状态与可见光状态两类状态下的焰色模型提取火焰疑似区域.对疑似火焰区域提取不规则度、角点量、闪烁频率和帧间相关性等4个静态及动态检测特征.通过减聚类和模糊C均值聚类相融合的方式优化训练样本,并分别训练2种状态下的火焰识别神经网络分类器.实验结果表明:视频类型判别平均准确率93.07%,21段火焰或干扰视频均能正确检测,报警时间小于8s,帧处理速度达到25帧/s以上.对室内自动火灾监控的精度高、抗干扰能力强、处理速度实时和适应全天候监控等.

In order to improve the accuracy and timeliness of fire detection and achieve the optimal utilization of existing hardware equipments, an automatic fire detection method with day-night infrared camera was proposed. Firstly, a video category classification algorithm based on IR frame color distribution in RGB color space was proposed. It can classify different videos and switch the detection state automatically. Then, fire candidate regions were extracted by using the fire color models of infrared state and visible state. Thirdly, the irregularity, corner number, frequency and correlation coefficient oscillation times were computed as four static or dynamic fire features. Finally, the training feature data optimized by Subtractive Clustering and Fuzzy C Means were the input of Neural Network,and fire classifiers for two kinds of video states were trained separately. Experiment results have indicated that the average classification accuracy of video categories has achieved 93.07%, and the proposed method is able to detect all of the 21 videos correctly. The proposed method gives warning 8 seconds after fire occurs. The processing speed is more than 25fps. It can meet the requirements of high detection precision, strong anti-interference, realtime processing speed, and adaptation to alleather monitoring.