基于分数阶微分视频融合的森林烟火检测算法

A forest fire smoke detection algorithm based on fractional-calculus video fusion

森林火灾检测是国内外林业应用研究的重要课题之一。及时准确地检测到森林火灾,对于森林健康及环境安全意义重大。现有的利用视频技术检测森林火灾的方法大多针对单一波段,如可见光波段或红外波段的视频信息进行分析,然而在实际应用过程中,由于森林环境复杂,基于单一波段视频信息检测火灾的结果欠佳。现阶段,基于多个波段的森林火灾检测方法非常少。本文综合利用红外及可见光视频特征,提出了一种基于分数阶微分视频融合的森林烟火检测算法,将分数阶微分理论引入红外视频和可见光视频融合中,利用分数阶微分算子对两个波段视频进行融合,然后利用背景去除法检测融合视频中的异常帧,且对异常帧图像及其与背景帧的差分图像分别进行图像分割,最终得到检测出的森林烟火区域。采用空间频率、平均梯度、森林火灾检测准确率和森林火灾检测时间误差度4个测度对本文算法和基于区域能量融合算法、基于窗口方差融合算法、基于HSI变换融合算法进行定量分析和比较。结果表明,本文算法的融合视频的融合效果最佳,并且森林火灾检测准确率和森林火灾检测时间误差均明显优于其他3种算法,说明本文提出的算法具有较好的有效性和准确性,为森林火灾检测提供了有利的新途径。

Forest fire detection is one of the most important researches in the forestry applications. It is essential for the safety and protection of forest if the detection of forest fire is timely and accurate. The existing forest fire detection methods using video analysis technology are almost based on the single band video such as the visible light video or infrared video. However,the detection results are not very robust depending on a single band video in the complex forest environment. At the present stage,the study of forest fire detection based on the several bands video is not deep. Using the features of forest fire extracted from the infrared video and visible light video,a new forest fire detection algorithm based on fractional-calculus theory was proposed. The fused video was achieved from the infrared video and the visible light video using the fractional-calculus operator. In the fused video,the background subtraction method was used to determine if there was an abnormal phenomenon occurred. This abnormal case is an indication of the forest fire possibility. Then,a threshold segmentation method was applied into the current frame and the difference image between the current frame and background frame if the fused video was abnormal. Finally,the forest fire region will be detected with these two segmented images. The proposed algorithm was compared with the regional energy fusion algorithm,regional variance fusion algorithm and HSI fusion algorithm using four different measures. Those measures include the spatial frequency,the average gradient,the detection accuracy rate and the detection time rate. Experimental results demonstrate that the proposed algorithm is efficient and accurate and it provides a favorable new approach for the forest fire detection.