| 早期油料火灾图像检测及识别技术研究 |
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| 引用本文： | 陈俊,杜扬,王冬,吕航.早期油料火灾图像检测及识别技术研究[J].计算机工程与科学,2010,32(2):72-74. |
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| 作者姓名： | 陈俊 杜扬 王冬 吕航 |
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| 作者单位： | 1. 解放军后勤工程学院军事供油工程系,重庆,400016
2. 中航油重庆分公司,重庆,401120 |
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| 摘 要： | 本文提出了一种早期油料火灾图像检测及识别算法。将火焰颜色、亮度及运动特征作为火灾检测与识别的判据,在火焰颜色模型和运动图像差分模型的基础上提出利用离散分形布朗随机增量场模型对早期油料火灾图像进行进一步的判定。模拟坑道实验结果表明,该算法能够有效提高油料火灾检测与识别的准确率,降低误报、漏报率。
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| 关 键 词： | 油料火灾图像 火焰模型 差分模型 离散分形布朗随机增量场模型 |
| 收稿时间： | 2008-10-05 |
| 修稿时间： | 2009-01-05 |
Research on the Detection and Identification Technology of Early Oil Fire Images |
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| CHEN Jun,DU Yang,WANG Dong,L Hang.Research on the Detection and Identification Technology of Early Oil Fire Images[J].Computer Engineering & Science,2010,32(2):72-74. |
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| Authors: | CHEN Jun DU Yang WANG Dong L Hang |
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| Affiliation: | 1.Department of Petroleum Supply Engineering/a>;PLA Logistical Engineering University/a>;Chongqing 400016/a>;2.Chongqing Branch of China National Aviation Fuel Groups Corporation/a>;Chongqing 401120/a>;China |
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| Abstract: | An algorithm of early oil fire image detection and recognition is put forward. The flame color,brightness and movement characteristics are chosen as the criteria. The early oil fire images are further detected and recognized by the algorithm of the Discrete Fractal Brownian Incremental Random Field model based on an analysis of the flame model and the differential model. The results of the simulated tunnel experiments show that the algorithm can successfully detect and recognize oil fire. |
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| Keywords: | oil fire image flame model differential model discrete fractal brownian incremental random field model (DFBIR) |
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