基于改進CV模型的煤礦井下早期火災圖像分割
發(fā)布時間：2018-09-10 18:17
【摘要】：煤礦井下早期火災圖像中火焰區(qū)域、火焰余輝及非火焰高灰度干擾區(qū)域三者的灰度值十分接近,利用傳統(tǒng)的Chan-Vese(CV)模型很難將火焰區(qū)域精確地提取出來。針對這一問題,提出了一種改進的CV模型以實現(xiàn)煤礦井下早期火災圖像的精確分割。在計算目標和背景區(qū)域擬合中心時,引入自適應權(quán)值進行加權(quán)平均,充分考慮了像素點灰度值與擬合中心的差異,并據(jù)此確定該點對擬合中心的貢獻度,更加精確地計算目標和背景區(qū)域的擬合中心;為了加速模型的演化,引入曲線內(nèi)外區(qū)域像素的中值絕對差,替換模型中的內(nèi)外區(qū)域能量系數(shù),提高模型分割效率。最終達到快速提取早期火災圖像中火焰區(qū)域的目的。大量實驗結(jié)果表明,與現(xiàn)有的Otsu算法、CV模型、引入能量權(quán)重的CV模型、引入梯度信息的CV模型以及兩種類似提出模型的CV模型相比,利用改進CV模型對煤礦井下早期火災圖像,能取得更好的分割效果,并且滿足實時性要求。
[Abstract]:The grayscale values of flame region, flame afterglow and non-flame high gray level interference region in early fire images of underground coal mine are very close, so it is difficult to extract the flame region accurately by using traditional Chan-Vese (CV) model. To solve this problem, an improved CV model is proposed to achieve accurate segmentation of early fire images in coal mines. When calculating the fitting center of the target and background region, the adaptive weight value is introduced to weighted average, and the difference between the pixel gray value and the fitting center is fully considered, and the contribution of the point to the fitting center is determined according to the difference between the gray value of the pixel point and the fitting center. In order to accelerate the evolution of the model, the absolute difference of the median value of the pixel inside and outside the curve is introduced to replace the energy coefficient of the inner and outer region of the model, and the efficiency of model segmentation is improved. Finally, it can quickly extract the flame region from the early fire image. A large number of experimental results show that compared with the existing Otsu algorithm and CV model, the CV model with energy weight, the CV model with gradient information and two similar CV models, the improved CV model is used to analyze the early mine fire images. It can achieve better segmentation effect and meet the real-time requirements.
【作者單位】： 南京航空航天大學電子信息工程學院;安徽理工大學煤礦安全高效開采省部共建教育部重點實驗室;
【基金】：煤礦安全高效開采省部共建教育部重點實驗室開放基金資助項目(JYBSYS2014102)
【分類號】：TD752;TP391.41
[Abstract]:The grayscale values of flame region, flame afterglow and non-flame high gray level interference region in early fire images of underground coal mine are very close, so it is difficult to extract the flame region accurately by using traditional Chan-Vese (CV) model. To solve this problem, an improved CV model is proposed to achieve accurate segmentation of early fire images in coal mines. When calculating the fitting center of the target and background region, the adaptive weight value is introduced to weighted average, and the difference between the pixel gray value and the fitting center is fully considered, and the contribution of the point to the fitting center is determined according to the difference between the gray value of the pixel point and the fitting center. In order to accelerate the evolution of the model, the absolute difference of the median value of the pixel inside and outside the curve is introduced to replace the energy coefficient of the inner and outer region of the model, and the efficiency of model segmentation is improved. Finally, it can quickly extract the flame region from the early fire image. A large number of experimental results show that compared with the existing Otsu algorithm and CV model, the CV model with energy weight, the CV model with gradient information and two similar CV models, the improved CV model is used to analyze the early mine fire images. It can achieve better segmentation effect and meet the real-time requirements.
【作者單位】： 南京航空航天大學電子信息工程學院;安徽理工大學煤礦安全高效開采省部共建教育部重點實驗室;
【基金】：煤礦安全高效開采省部共建教育部重點實驗室開放基金資助項目(JYBSYS2014102)
【分類號】：TD752;TP391.41
【參考文獻】
相關(guān)期刊論文 前10條
1 商蕊;方俊;王靜舞;;高原低壓環(huán)境小尺寸池火圖像特征[J];燃燒科學與技術(shù);2016年04期
2 吳詩Zs;吳一全;周建江;孟天亮;;利用倒數(shù)灰度熵和改進Chan-Vese模型進行SAR河流圖像分割[J];測繪學報;2015年11期
3 王相海;王金玲;方玲玲;;灰度差能量函數(shù)引導的圖像分割自適應C-V模型[J];模式識別與人工智能;2015年03期
4 孫傳猛;曹樹剛;李勇;;基于LBF改進模型的煤巖細觀結(jié)構(gòu)研究[J];煤炭學報;2015年02期
5 宋昱;吳一全;畢碩本;;邊緣修正CV模型的衛(wèi)星遙感云圖分割方法[J];光學學報;2014年09期
6 韓郁，
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