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  • 學位論文

基於 Raspberry Pi 之整合火災偵測與警示系統

Integrated Fire Detection And Alarm System Using Raspberry Pi

指導教授 : 張元翔 蔡明達

摘要


早期火焰偵測是預防火災事故危害生命和財產的關鍵,在此論文中提出了基於 Raspberry Pi 之整合火災偵測與警示系統,並且此系統可以結合在物聯網(IOT)的應用中。我們的系統是整合嵌入式系統、傳感器以及視訊攝影機合併視覺型的火焰偵測的技術。在我們的系統中是使用氣體感測器來檢測易燃氣體洩漏和火焰傳感器來檢測火焰。此外還有基於視覺型的火焰偵測方法來檢測火災場景的火焰,此方法可以用以下步驟,影像前處理、前景區域分析、火焰動態行為分析及火焰流動能像分析來說明。實驗研究顯示了我們的系統已經實現了93%的敏感度和84%的有效性。綜合以上所述,我們提出了一種火焰偵測的設備,比傳統的火(或煙霧)探測器還具有淺在優勢,另如改進早期火焰偵測和具有成本效益等,可用於家庭或工業方面。

並列摘要


Early detection of fire is the key to prevent loss of life and property caused by fire disasters. An integrated fire detection and alarm system using Raspberry Pi is presented and can be incorporated in the Internet of Things (IOT) applications. Our system integrates technology of embedded systems, sensors, and vision-based fire detection method using webcam. In our system, gas sensors and flame sensors that are sensitive to gas leakage and flame are used. In addition, our vision-based fire detection method is sensitive to bursting fire scenarios and can be described in the following steps, namely image preprocessing, foreground region analysis, fire dynamic behavior analysis, and fire flow energy analysis. The experimental studies demonstrate that our system has achieved the sensitivity of 93% and the specificity of 84%, respectively. In summary, we have presented an alternative strategy to fire detection and alarm that has potential advantages over conventional fire (or smoke) detectors, e.g., improved and early detection of fire, cost-effective, etc., for home or industrial usage.

參考文獻


[1] Q. Yu, D. Zheng, Y. Fu, and A. Dong, “Intelligent Fire Alarm System Based on Fuzzy Neural Network,” Intelligent Systems and Applications, pp. 1-4, May 2009.
[3] T. H. Chen, Y. H. Yin, S. F. Huang and Y. T. Ye “The smoke detection for early fire-alarming system based on video processing,” 2006 International Conference on Intelligent Information Hiding and Multimedia, December 2006.
[4] T. Y. Lai, J. Y. Kuo, Y. Y. FanJiang, S. P. Ma and Y. H. Liao, “Robust Little Flame Detection on Real-Time Video Surveillance System,” Innovations in Bio-Inspired Computing and Applications (IBICA), pp. 139-143, September 2012.
[7] M. S. B. Bahrudin, R. A. Kassim and N. Buniyamin, “Development of Fire alarm system using Raspberry Pi and Arduino Uno,” Electrical, Electronics and System Engineering (ICEESE), pp. 43-48, December 2013.
[9] G. Bradski, and A. Kaehler, Learning OpenCV: Computer vision with the OpenCV library, 2008, O'Reilly Media, inc.

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