矿业科学学报2025，Vol.10Issue(1)：116-124,9.DOI:10.19606/j.cnki.jmst.2024930
基于红外视觉特征融合的矿井外因火灾监测方法
Mine exogenous fire monitoring method using the fusion of infrared visual features
摘要
Abstract
In order to solve the problems of high false positive and false negative rates of external fire monitoring in complex mine environments,a monitoring algorithm using infrared visual feature fusion was proposed.Firstly,the Local Contrast Measure(LCM)model for infrared small target detection was improved to enhance the saliency of early-stage fire targets,thereby segmenting out suspected fire are-as.Then,by analyzing the visual features of exogenous fires and major interfering heat sources in ther-mal infrared image sequences under different surveillance scenarios,the salient features of fires with strong anti-interference ability were preferred.Next,fire salient feature extraction methods and similari-ty estimation strategies were optimized to obtain the main visual features of suspected fire areas in the thermal infrared image sequences and construct a fire feature vector.Finally,by establishing a feature vector set and constructing a mine exogenous fire detection model using Support Vector Machine(SVM),the proposed algorithm was experimentally validated.The results show that the proposed algo-rithm realizes exogenous fire monitoring in different scenarios,as well as in remote and early stages,with accuracy and detection rates of 96.93%and 96.24%,respectively,and a false detection rate of 2.56%.Compared to the described comparison algorithms,the proposed method has better improve-ments in the accuracy,false alarm rate,and leakage alarm rate of fire monitoring.关键词
矿井外因火灾/红外视觉特征/局部对比度度量(LCM)模型/特征向量/支持向量机(SVM)Key words
mine exogenous fire/infrared visual features/local contrast measure(LCM)model/eig-envector/support vector machine(SVM)分类
矿业与冶金引用本文复制引用
李晓宇,范伟强,刘毅,霍跃华..基于红外视觉特征融合的矿井外因火灾监测方法[J].矿业科学学报,2025,10(1):116-124,9.基金项目
国家自然科学基金(52364017) （52364017）
内蒙古自治区高等学校科学研究基金(NJZY23056) （NJZY23056）
