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研究生:許家昌
研究生(外文):Jia-Chang Shiu
論文名稱:應用影像技術於煙霧偵測之研究
論文名稱(外文):A Study of Smoke and Fog Detection Using Image Processing Technique
指導教授:吳明川吳明川引用關係
口試委員:徐勝均陳政順
口試日期:2012-07-27
學位類別:碩士
校院名稱:國立臺北科技大學
系所名稱:機電整合研究所
學門:工程學門
學類:機械工程學類
論文種類:學術論文
論文出版年:2012
畢業學年度:100
語文別:中文
論文頁數:59
中文關鍵詞:煙霧檢測霧氣檢測
外文關鍵詞:Smoke DetectionFog Detection
相關次數:
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煙霧影像偵測技術能用於多種場景,不論是由氣候潮濕所產生的霧氣,亦或是由燃燒的微粒所產生的煙霧,能夠用於監測道路、港口、機場等需要良好的視野與監控。本研究將水氣與燃燒所產生的兩種影像技術整合,完成一套即時煙霧偵測系統。
首先根據燃燒後所產生的煙霧作偵測,由顏色區分出煙霧的區域,再由運動歷史軌跡演算法,區別畫面中的移動物體,煙霧有向四周擴散的特性,檢驗區域中飽合度的變化,最後由於燃燒的煙霧溫度較高,有向上移動的特性,由此特性來避免誤抓,對於潮濕所產生的霧氣,由其影像模糊特性,作快速傅立葉轉換,判別出影像清晰程度,再以顏色區別出煙霧的區域。


Smoke image detection technology is a widely used application, whether for humid climate, fog, smoke or combustion particles. It''s also used for many monitoring scenes, such as highway, air port, port, etc. In this study, the water vapor and burning images are integrated to establish a real-time detection system.
First, detect the smoke generated by combustion, and color-code the areal differentiate of the smoke accordingly. Then use the Motion History Image (MHI) algorithm to differentiate the moving objects on the screen. Smoke spreads to the surrounding properties. Test changes in the regional saturation. Since high temperature of the burning smoke makes the particle move upward. To avoiding mistaken detection is essential. Moist from fog blurs the image and then by using Fast Fourier Transform (FFT) to distinguish image clarity, the color difference between the regions can be accurately established.


摘 要 i
ABSTRACT ii
誌 謝 iii
目 錄 iv
表 目 錄 vi
圖 目 錄 vii
第一章 緒論 1
1.1前言 1
1.2研究動機 3
1.3研究目的 5
1.4文獻回顧 6
1.5論文架構 8
第二章 數位影像處理技術 9
2.1色彩空間介紹 9
2.1.1 HSV色彩空間 10
2.1.2 YCrCb色彩空間 12
2.2空間域濾波 13
2.2.1中值濾波器(Median Filter) 14
2.3傅立葉轉換 15
2.4影像二值化 17
2.5形態學 18
2.5.1 膨脹運算 18
2.5.2 侵蝕運算 19
2.5.3 斷開運算 20
2.5.4 閉合運算 20
2.6連通標記 21
第三章 實驗設備與偵測流程 24
3.1實驗設備 24
3.1.1 PTZ攝影機 24
3.1.2數位照相機 25
3.1.3個人電腦與程式開發軟體 26
3.2偵測流程 27
第四章 煙霧影像偵測 29
4.1煙霧顏色特徵 29
4.2燃燒煙霧偵測 30
4.3 移動物件偵測 31
4.3.1 連續影像相減法 31
4.3.2 連續影像相減累積法 32
4.3.3 MHI演算法 32
4.4 飽和度變化累計 34
4.5 煙霧方向判定 36
4.6 霧氣偵測 38
4.7高斯霧氣模型 38
4.8霧氣飽和度累計 42
第五章 實驗結果與討論 44
5.1實驗樣本介紹 44
5.2實驗結果 46
第六章結論與未來展望 51
6.1結論 51
6.2未來展望 52
參考文獻 53
附錄 55
附錄A SONY SNC-RZ30規格表 55
附錄B SONY SNC-RZ30 CGI Command List 57


[1]Paolo Piccinini, Simone Calderara, Rita Cucchiara, “Reliable Smoke Detection System in the Domains of Image Energy and Color,” ICIP, 2008.
[2]Hidenori Maruta, Yasuharu Katot, Akihiro Nakamural, and Fujio Kurokawall, “Smoke Detection in Open Areas Using Its Texture Features and Time Series Properties,” IEEE International Symposium on Industrial Electronics, 2009.
[3]Chao-Ching Ho, Tzu-Hsin Kuo, “Real-Time Video-Based Fire Smoke Detection System,” International Conference on Advanced Intelligent Mechatronics Suntec Convention and Exhibition Center Singapore, July 14-17, 2009.
[4]Guillemant, P., Vicente, J., “Real-Time Identification of Smoke Images by Clustering Motions on a Fractal Curve with a Temporal Embedding Method,” Society of Photo-Optical Instrumentation Engineers, pp.554–563, April 2001.
[5]Thou-Ho (Chao-Ho) Chen, Yen-Hui Yin, Shi-Feng Huang and Yan-Ting Ye, “The Smoke Detection for Early Fire-Alarming System Base on Video Processing,” International Conference on Intelligent Information Hiding and Multimedia Signal Processing, 2006.
[6]Feiniu Yuan, “A Fast Accumulative Motion Orientation Model Based on Integral Image for Video Smoke Detection,” Pattern Recognition Letters 29, 2008, pp.925-932.
[7]A. J. Lipton, H. Fujiyoshi, and R. S. Patil, “Moving Target Classification and Tracking from Real-Time Video,” IEEE Workshop Applications of Computer Vision, pp.8-14, 1998.
[8]R. Cutler and L. S. Davis, “Robust Real-Time Periodic Motion Detection, Analysis, and Applications,” IEEE Trans. Pattern Anal. Machine Intell., vol. 22, pp.781-796, Aug. 2000.
[9]S. C. Cheung and C. Kamath, “Robust Techniques for Back Ground Subtraction in Urban Traffic Video,” Proceedings of SPIE, 2004.
[10]B. K. P. Horn and B. G. Schunck, “Determining Optical Flow,” Artificial Intelligence, Vol. 17, pp.185-203, 1981.
[11]Jinglei Tang, Zhiyi Zhang, Jing Xin, and Weijie Lei, “Research on Background Reconstruction Based on the Judging of Model Categories,” IEEE, WRI World Congress on Software Engineering(WCSE''09), 2009.
[12]繆紹剛,數位影像處理,台北:普林斯頓國際有限公司,2007。
[13]Ming-Wei, An ; Zong-Liang, Guo ; Jibin, Li ; Tao, Zhou, “Visibility Detection Based on Traffic Camera Imagery,” Information Sciences and Interaction Sciences (ICIS), 2010 3rd International Conference on Issue Date ,23-25 June 2010.
[14]S. Bronte, L. M. Bergasa, P. F. “ Fog Detection System Based on Computer Vision Techniques”, Spain Proceedings of the 12th International IEEE Conference on Intelligent Transportation Systems, St. Louis, MO,USA, October 3-7, 2009.
[15]Andrea Lagorio, Enrico Grosso, Massimo Tistarelli, “Automatic Detection of Adverse Weather Conditions in Traffic Scenes,” Advanced Video and Signal Based Surveillance, 2008. AVSS ''08. IEEE Fifth International Conference on
Digital Object Identifier: 10.1109/AVSS.2008.50.
[16]連國珍,數位影像處理,台北:儒林圖書公司,2004。
[17]G.. Bradski and A. Kaebler, Learning OpenCV Computer Vision with the OpenCV Library, America: O`Reilly, 2008.


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