华东师范大学学报（自然科学版） >

2023 , Vol. 2023 >Issue 5: 147 - 163

DOI: https://doi.org/10.3969/j.issn.1000-5641.2023.05.013

数据分析

基于空间域和频率域方法的烟雾检测

  • 盛连军 ,
  • 汤致轩 ,
  • 茅晓亮 ,
  • 白帆 ,
  • 黄定江
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  • 1. 国网上海市电力公司 奉贤供电公司, 上海　201499
    2. 华东师范大学 数据科学与工程学院, 上海　200062
    3. 上海深其深网络科技有限公司, 上海　200439

收稿日期: 2023-07-01

  网络出版日期: 2023-09-20

基金资助

国家自然科学基金(62072185)

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Smoke detection based on spatial and frequency domain methods

  • Lianjun SHENG ,
  • Zhixuan TANG ,
  • Xiaoliang MAO ,
  • Fan BAI ,
  • Dingjiang HUANG
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  • 1. Fengxian Power Supply Company, State Grid Shanghai Electric Power Company, Shanghai　201499, China
    2. School of Data Science and Engineering, East China Normal University, Shanghai　200062, China
    3. Shanghai Thinking Things Network Technology Co. Ltd., Shanghai　200439, China

Received date: 2023-07-01

  Online published: 2023-09-20

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摘要

变电站等工业场景中, 基于监控视频的视觉烟雾检测已被作为一种新的环境辅控方式, 用于辅助或代替基于烟雾传感器的烟雾检测. 但是, 工业应用中要求视觉烟雾检测算法在保证误检率低的基础上, 要尽可能降低漏检率. 针对该问题, 基于空间域和频率域方法, 提出了一种新的烟雾检测算法, 分别在空间域和频率域进行烟雾检测: 在空间域上, 在提取烟雾运动特性的基础上, 设计了提取烟雾“蒙版特性”的方法, 以保证较低的漏检率; 在频率域上, 分别结合滤波模块和神经网络模块, 以进一步降低误检率. 最后通过融合后处理策略, 得到最终检测结果, 从而平衡漏检率和误检率. 在测试数据集上, 所提烟雾检测算法的误检率达到了0.053, 漏检率达到了0.113, 实现了误检率和漏检率的良好平衡. 所提检测方法适用于变电站等实际工业场景的烟雾检测.

关键词： 视觉烟雾检测; 空间域和频率域; 神经网络; 三维傅里叶变换

本文引用格式

盛连军 , 汤致轩 , 茅晓亮 , 白帆 , 黄定江 . 基于空间域和频率域方法的烟雾检测[J]. 华东师范大学学报（自然科学版）, 2023 , 2023(5) : 147 -163 . DOI: 10.3969/j.issn.1000-5641.2023.05.013

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

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