光学精密工程2024，Vol.32Issue(8)：1186-1198,13.DOI:10.37188/OPE.20243208.1186
基于低对比度红外图像时空信息的气体泄漏检测
Gas leakage detection based on spatiotemporal information of low contrast infrared images
摘要
Abstract
The hazards caused by gas leakage accident are multifaceted,such as environmental pollution,personnel and property loss,fire and explosion.Thermal infrared imaging is widely used as a qualitative detection technology that can realize large-scale and fast imaging.However,compared with general infra-red image,the contrast of gas cloud infrared image is lower,the edge is more blurred,and it's hard to de-tection.To solve this problem,this article proposed a leak detection method for low contrast gas infrared images based on mixed Gaussian background modeling.Firstly,in the preprocessing stage,time-domain adaptive interframe filtering algorithm was proposed to realize noise reduction and detail maintenance of in-frared images.Then,based on spatial information and gradient information constraints,a spatiotemporal mixed Gaussian background model was proposed to achieve preliminary extraction of the foreground of leaked gas targets.Finally,to better remove interfering moving targets in foreground detection,an im-proved fast and robust fuzzy C-means clustering method was used to realize adaptive segmentation of gas regions.The experimental results show that at the leakage distance of 5 m,this detection algorithm can ef-fectively improve accuracy,compensate for the problems of gas region voids,and reduce interference from other moving objects.The accuracy of gas leakage detection is between 92.3%and 96.3%,which has significant anti-interference and region segmentation capabilities compared to other algorithms.关键词
气体泄漏检测/红外热成像/时空高斯混合模型/时域自适应帧间滤波/运动检测/快速和鲁棒的模糊C均值聚类Key words
gas leakage detection/infrared thermal imaging/spatiotemporal Gaussian mixture model/time-domain adaptive inter frame filtering/moving detection/fast and robust fuzzy C-means clustering分类
计算机与自动化引用本文复制引用
左金辉,徐文斌,周世杰,盛道斌,徐向东,李正强,韩颖慧,吴春江,张磊..基于低对比度红外图像时空信息的气体泄漏检测[J].光学精密工程,2024,32(8):1186-1198,13.基金项目
国家重点研发计划(No.2022YFE0209500) （No.2022YFE0209500）
国家重点研发计划(No.2023YFB3907405) （No.2023YFB3907405）
