Smoke detection based on spatial and frequency domain methods

doi:10.3969/j.issn.1000-5641.2023.05.013

Abstract

Abstract:

In industrial scenarios such as substations, video-based visual smoke detection has been adopted as a new environmental monitoring method to assist or replace smoke sensors. However, in industrial applications, visual smoke detection algorithms are required to maintain a low false detection rate while minimizing the missed detection rate. To address this, this study proposes a smoke detection algorithm based on spatial and frequency domain methods, which perform smoke detection in both domains. In the spatial domain, in addition to extracting smoke motion characteristics, this study designed a method for extracting smoke mask characteristics, which effectively ensures a low missed detection rate. In the frequency domain, this study combined filtering and neural network modules to further reduce the false detection rate. Finally, a fusion domain post-processing strategy was designed to obtain the final detection results. In experiments conducted on a test dataset, the smoke detection algorithm achieved a false detection rate of 0.053 and missed detection rate of 0.113, demonstrating a good balance between false alarms and missed detections, which is suitable for smoke detection in substation industrial scenes.

Key words: visual smoke detection, spatial and frequency domains, neural network, three-dimensional Fourier transform

CLC Number:

TP391

Lianjun SHENG, Zhixuan TANG, Xiaoliang MAO, Fan BAI, Dingjiang HUANG. Smoke detection based on spatial and frequency domain methods[J]. Journal of East China Normal University(Natural Science), 2023, 2023(5): 147-163.

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算法	误检率	漏检率
基线算法	0.776	0.013
基线算法 + 蒙版特性	0.671	0.025
基线算法 + 蒙版特性 + 频域滤波	0.066	0.088
基线算法 + 蒙版特性 + 频域滤波 + 三维神经网络	0.053	0.113

算法	误检率	漏检率
基于小波变换的烟雾检测算法^[4]	0.115	0.184
基于纹理分析的烟雾检测算法^[5]	0.092	0.106
基于改进YOLOv5的烟雾检测算法^[16]	0.079	0.173
本文算法	0.053	0.113

算法	单张图片平均推理时间/ms
算法	在GPU上	在CPU上	在NPU上
基于小波变换的烟雾检测算法^[4]	42	50	–
基于纹理分析的烟雾检测算法^[5]	37	42	–
基于改进YOLOv5的烟雾检测算法^[16]	55	170	68
本文算法	48	161	65

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