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基于多光谱图像的火灾烟雾检测

[Detecting fire smoke based on the multispectral image].

作者信息

Wei Ying-Zhuo, Zhang Shao-Wu, Liu Yan-Wei

机构信息

College of Automation, Northwestern Polytechnical University, Xi'an 710072, China.

出版信息

Guang Pu Xue Yu Guang Pu Fen Xi. 2010 Apr;30(4):1061-4.

Abstract

Smoke detection is very important for preventing forest-fire in the fire early process. Because the traditional technologies based on video and image processing are easily affected by the background dynamic information, three limitations exist in these technologies, i. e. lower anti-interference ability, higher false detection rate and the fire smoke and water fog being not easily distinguished. A novel detection method for detecting smoke based on the multispectral image was proposed in the present paper. Using the multispectral digital imaging technique, the multispectral image series of fire smoke and water fog were obtained in the band scope of 400 to 720 nm, and the images were divided into bins. The Euclidian distance among the bins was taken as a measurement for showing the difference of spectrogram. After obtaining the spectral feature vectors of dynamic region, the regions of fire smoke and water fog were extracted according to the spectrogram feature difference between target and background. The indoor and outdoor experiments show that the smoke detection method based on multispectral image can be applied to the smoke detection, which can effectively distinguish the fire smoke and water fog. Combined with video image processing method, the multispectral image detection method can also be applied to the forest fire surveillance, reducing the false alarm rate in forest fire detection.

摘要

烟雾检测在火灾早期预防森林火灾方面非常重要。由于基于视频和图像处理的传统技术容易受到背景动态信息的影响,这些技术存在三个局限性,即抗干扰能力较低、误检率较高以及火灾烟雾和水雾不易区分。本文提出了一种基于多光谱图像的烟雾检测新方法。利用多光谱数字成像技术,在400至720纳米的波段范围内获取了火灾烟雾和水雾的多光谱图像序列,并将图像划分为多个频段。将各频段之间的欧几里得距离作为表示频谱图差异的一种度量。在获得动态区域的光谱特征向量后,根据目标与背景之间的频谱图特征差异提取火灾烟雾和水雾区域。室内和室外实验表明,基于多光谱图像的烟雾检测方法可应用于烟雾检测,能够有效区分火灾烟雾和水雾。结合视频图像处理方法,多光谱图像检测方法也可应用于森林火灾监测,降低森林火灾检测中的误报率。

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