Research on forest fire smoke detection technology based on video region dynamic features
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摘要:
目的 视频监控越来越多地应用到森林火灾烟雾的早期检测中。现有的视频林火烟雾检测方法大多是基于像素提取烟雾特征进行分析检测，烟雾发生早期或烟雾距离摄像头较远时，在视频图像上烟雾仅呈现较小区域，且烟雾的扩散具有无规则性，背景环境复杂多变，导致基于像素的特征不明显，因此使基于像素的烟雾自动化检测难度增大。本文根据可见光视频图像处理原理，提出一种基于局部区域图像动态特征的林火视频烟雾检测方法，以提高林火视频烟雾检测准确度和灵敏度。 方法 以视频图像为研究对象，每秒取一帧生成图像序列，对图像序列进行多层次不同尺度分区；利用图像信噪比原理，计算分区后的连续图像序列的信噪比；根据背景图像信噪比得到自适应阈值，确定待检测图像序列发生亮度变化的图像块，即为疑似烟雾块；提取疑似烟雾块的LBP纹理特征，采用支持向量机区分出烟雾区域。 结果 利用HSV颜色空间的亮度分量，可以有效提取烟雾区域。选择有林火烟雾的视频，对提出的烟雾变化检测方法进行验证，分析结果表明该方法能确定烟雾发生所在的图像块，且能排除部分非烟雾干扰因素。 结论 本文提出了基于局部区域亮度特征和LBP纹理特征的视频林火烟雾检测技术，能准确定位烟雾发生区域，排除部分干扰因素，检测识别率平均达到92%以上，有助于实时林火烟雾自动检测，提高林火烟雾检测率，具有很强的实用性。 Abstract:Objective Video surveillance is increasingly applied to the early detection of forest fire smoke. The existing video forest fire smoke detection methods are mostly based on pixel extraction of smoke characteristics for analysis and detection, but when the smoke is early or the smoke is far from the camera, the smoke only appears in a small area on the video image. Moreover, the diffusion of smoke is irregular, and the background environment is complex and changeable, resulting in insignificant pixel-based features, which makes it more difficult to automatically detect pixel-based smoke. Based on the principle of visible light video image processing, this paper proposes a forest fire video smoke detection method based on local area image dynamic characteristics to improve the accuracy and sensitivity of forest fire video smoke detection. Method The video images were selected as the research object. One frame per second was taken to generate an image sequence, and the image sequences were divided into multiple levels and different scales; using the principle of image signal-to-noise ratio, we calculated the signal-to-noise ratio of continuous image sequences after blocking; the adaptive threshold was obtained according to the signal-to-noise ratio of the background image, and the image block whose brightness changes in the image sequence to be detected was determined to be the suspected smoke block; the LBP texture feature of the suspected smoke block was extracted, and the support vector machine was used to distinguish the smoke area. Result Using the value component of the HSV color space, smoke areas can be effectively extracted. The videos with forest fire smoke were selected to verify the proposed smoke change detection method. The analysis results showed that the method can determine the image block where the smoke occurred and excluded some non-smoke interference factors. Conclusion This paper proposes a video forest fire smoke detection technology based on brightness characteristics and LBP texture features of local area, which can accurately locate the smoke occurrence area and exclude some interference factors. The average detection recognition rate reaches more than 92%, which is helpful for real-time forest fire smoke automatic detection and improving the detection rate of forest fire smoke. It has a strong practicality. -
Key words:
- forest fire smoke detection /
- video image /
- block /
- signal-to-noise ratio /
- LBP texture feature
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图 4 烟雾HSV颜色通道SNR变化趋势
m表示参考帧取背景帧；n表示参考帧取前一帧。m indicates the reference frame taking the background frame; n indicates the reference frame taking the previous frame.
Figure 4. SNR changing trend of smoke HSV color channel
图 5 SNR变化趋势
SNRbg表示前一时间窗口内SNR的值。SNRbg indicates the value of SNR in the previous time window.
Figure 5. Changing trend of SNR
表 1 视频相关信息
Table 1. Video related information
编号
No.类别
Classification视频名称
Video name视频场景描述
Video scene description帧速率/(帧·s−1)
Frame rate/
(frame·s−1)截取后总帧数
Total number of frames after clip视频1 Video1 烟雾
SmokePelco_Colakli 远距离，有风，烟雾扩散缓慢
Long distance, windy, smoke spread slowly7 122 视频2 Video2 烟雾
Smoke20070817 Aksehir_Duman_Test5_
en_Iyisi_near_487_frame远距离，有风，烟雾扩散缓慢
Long distance, windy, smoke spread slowly7 484 视频3 Video3 烟雾
SmokeSmoke_Manavgat_Raw 远距离，烟雾较浓，扩散缓慢
Long distance, thick smoke, slow diffusion25 242 视频4 Video4 烟雾
Smoke20090409 ManavgatTEst 远距离，有风，有车辆驶过，烟雾扩散缓慢
Long distance, windy, vehicles passing by, smoke spread slowly9 180 视频5 Video5 烟雾
Smoke森林保护站拍摄视频1
Video1 taken at the forest protection station近距离，有烟雾，卡车施工作业
Close distance, smoke, truck construction operation25 238 视频6 Video6 非烟雾
Non-smoke森林保护站拍摄视频2
Video2 taken at the forest protection station近距离，有云
Close distance, cloudy30 220 视频7 Video7 非烟雾
Non-smoke森林保护站拍摄视频3
Video3 taken at the forest protection station近距离，有行人经过
Close distance, pedestrians passing by24 192 视频8 Video8 非烟雾
Non-smoke森林保护站拍摄视频4
Video4 taken at the forest protection station近距离，有车辆驶过
Close distance, vehicles passing by25 168 
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表 2 2 × 2分块方式
Table 2. 2 × 2 block pattern
编码 No. 块0 Block 0 块1 Block 1 块2 Block 2 块3 Block 3 待检测区域信噪比的方差SNRvar SNRvar of the area to be detected 5.477 5 0.041 3 0.054 2 0.053 1
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表 4 8 × 8分块方式
Table 4. 8 × 8 block pattern
编码 No. 块030 Block 030 块031 Block 031 块032 Block 032 块033 Block 033 待检测区域信噪比的方差SNRvar SNRvar of the area to be detected 21.453 7 16.541 4 25.686 1 40.761 4
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表 3 4 × 4分块方式
Table 3. 4 × 4 block pattern
编码 No. 块00 Block 00 块01 Block 01 块02 Block 02 块03 Block 03 待检测区域信噪比的方差SNRvar SNRvar of the area to be detected 0.083 5 1.738 1 0.677 1 14.888 5
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表 5 测试结果
Table 5. Test results
% 烟雾视频数据 Smoke video data 精确率 Precision (P) F1 视频1 Video1 90 95 视频2 Video2 93 96 视频3 Video3 90 95 视频4 Video4 95 98 视频5 Video5 93 96 注：F1为精确率和召回率的调和平均数。Note: F1 is harmonic mean of precision and recall.
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