红外与激光工程
紅外與激光工程
홍외여격광공정
INFRARED AND LASER ENGINEERING
2014年
1期
338-344
,共7页
韩郁翀%秦俊%马兴鸣%赵兰明%李雨农
韓鬱翀%秦俊%馬興鳴%趙蘭明%李雨農
한욱충%진준%마흥명%조란명%리우농
火焰识别%深度图%飞行时间%火灾探测
火燄識彆%深度圖%飛行時間%火災探測
화염식별%심도도%비행시간%화재탐측
fire flame identification%depth map%time of flight%fire detection
为开发飞行时间算法在火灾探测中的应用,简化算法,提高检测速率和准确性,根据飞行时间法,结合火焰的深度图特征,设计了基于深度图像变化率的火焰识别算法。以三维深度相机为主要图像捕获设备,进行了多组火焰识别实验,包括正庚烷火焰、乙醇火焰、纸张火焰、灯光干扰、行人干扰实验,对捕获的图像进行了处理与计算,提出了识别火焰的简化算法和火焰像素估计模型。采用该方法分析了火焰深度图特征,火焰识别结果图像的频谱图特征、集中度特征以及面积变化特征。研究结果表明,采用文中提出的算法的实验识准率大于91.5%,误识率小于3.8%,能有效识别火焰。
為開髮飛行時間算法在火災探測中的應用,簡化算法,提高檢測速率和準確性,根據飛行時間法,結閤火燄的深度圖特徵,設計瞭基于深度圖像變化率的火燄識彆算法。以三維深度相機為主要圖像捕穫設備,進行瞭多組火燄識彆實驗,包括正庚烷火燄、乙醇火燄、紙張火燄、燈光榦擾、行人榦擾實驗,對捕穫的圖像進行瞭處理與計算,提齣瞭識彆火燄的簡化算法和火燄像素估計模型。採用該方法分析瞭火燄深度圖特徵,火燄識彆結果圖像的頻譜圖特徵、集中度特徵以及麵積變化特徵。研究結果錶明,採用文中提齣的算法的實驗識準率大于91.5%,誤識率小于3.8%,能有效識彆火燄。
위개발비행시간산법재화재탐측중적응용,간화산법,제고검측속솔화준학성,근거비행시간법,결합화염적심도도특정,설계료기우심도도상변화솔적화염식별산법。이삼유심도상궤위주요도상포획설비,진행료다조화염식별실험,포괄정경완화염、을순화염、지장화염、등광간우、행인간우실험,대포획적도상진행료처리여계산,제출료식별화염적간화산법화화염상소고계모형。채용해방법분석료화염심도도특정,화염식별결과도상적빈보도특정、집중도특정이급면적변화특정。연구결과표명,채용문중제출적산법적실험식준솔대우91.5%,오식솔소우3.8%,능유효식별화염。
In order to develop the application of time-of-flight algorithm in fire detection and simplify the algorithm to improve detection rate and accuracy, according to the time-of-flight-depth-map method, considering with the characteristics of depth map of fire flame, fire flame identification algorithm based on variation rate of time-of-flight-depth-map was designed. Several groups of fire flame identification experiments, including n-heptane flame, ethanol flame, paper flame, lamplight interference and pedestrian interference test, were carried out with 3-D depth camera acted as main equipment. The captured maps were processed and computed. A simplified algorithm was proposed for fire flame identification which was used to analyze the characteristics of depth map, frequency spectrogram, concentration ratio and area fluctuation of fire flame. The results indicate that the identification precision rate is greater than 91.5%, and the misrecognition rate is less than 3.8%. Fire flame could be efficiently identified with this algorithm.