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.
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.
作者
盛连军
汤致轩
茅晓亮
白帆
黄定江
SHENG Lianjun;TANG Zhixuan;MAO Xiaoliang;BAI Fan;HUANG Dingjiang(Fengxian Power Supply Company,State Grid Shanghai Electric Power Company,Shanghai 201499,China;School of Data Science and Engineering,East China Normal University,Shanghai 200062,China;Shanghai Thinking Things Network Technology Co.Ltd.,Shanghai 200439,China)
出处
《华东师范大学学报（自然科学版）》
CAS
CSCD
北大核心
2023年第5期147-163,共17页
Journal of East China Normal University(Natural Science)
基金
国家自然科学基金(62072185)。
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
visual smoke detection
spatial and frequency domains
neural network
three-dimensional Fourier transform
