摘要：

针对城镇森林交界域火灾烟雾视频检测准确率低问题,提出一种融合多项图像特征和深度学习的视频烟雾检测算法。通过ViBe方法提取前景变化区域,根据烟雾模糊特征和角点信息排除部分纹理细节较明显的区域。在此基础上,以颜色特征为判据进一步缩小检测范围,使用累积帧差法排除运动刚体的干扰,利用深度学习模型识别目标是否为烟雾。采用级联分类器的方式设计整体算法,并使用并行计算技术进行实现。实验结果和工程案例表明,该算法能够实现城镇森林交界域火灾早期烟雾的精准识别。

关键词: 视频烟雾检测, 城镇森林交界域, 背景建模, 颜色特征, 深度学习

Abstract:

In order to improve the precision rate of video smoke detection for wildland-urban interface,a video smoke detection algorithm based on multiple image features and deep learning is proposed.Firstly,ViBe(Visual Background extrator)method is applied to extract the moving or changing foreground areas in surveillance videos and image corner information is used to exclude the disturb from objects with detail texture.Secondly,color space features are utilized to narrow the check area for smoke.Thirdly,the cumulated difference image is calculated to find and eliminate the influence of rigid bodies.At last,the deep learning model is used to recognize the filtered image area.The algorithm is designed in the framework of cascade classifier and implemented by using parallel computing technology.Experimental results and project cases verify that the proposed algorithm is significant for improving the precision rate of smoke detection for wildland-urban interface.

Key words: video smoke detection, wildland-urban interface, background modeling, color feature, deep learning

中图分类号: 

• TP751.1