以飽和度與亮度進行電腦視覺煙霧辨識研究

Study of Smoke Recognition by Computer Vision Using Saturation and Intensity

蒲永仁(Yong-Ren Pu)；施安鍵(An-Chien Shih)；李素幸(Su-Hsing Lee)

煙霧 ； 飽和度 ； 亮度 ； 影像處理 ； 電腦視覺 ； Smoke ； Saturation ； Intensity ； Image processing ； Computer vision

勞動及職業安全衛生研究季刊

23卷1期（2015 / 03 / 15）

16 - 24

繁體中文

火災探測具有悠久的歷史，傳統的感熱式與偵煙式探測器係分別利用火焰產生的熱與煙來觸發作動，而火焰式探測器則以火焰發出之紅外光或紫外光強度來作動，這些探測器絕大多數都是火警自動警報設備的一環，無法單獨設置，而且因探測原理的限制，易受環境干擾而延遲或誤報；此外，多數火災發生之初，首先產生大量煙霧，瀰漫整個場所，人員往往因此能夠在遠處察覺災害的發生。近年來電腦視覺產品成本的大幅下降，監視器材運用日漸廣泛，以可見光直接監控工作場所火災或煙霧的產生，精確的判別火災的發生而減少誤報，則成為必然的趨勢。本研究擬利用圖控式程式語言研發出一套易於操作之煙霧監視程式，利用視覺直觀方式將監視影像之RGB色彩系統轉換為HSI系統，首先將所有像素資料初始化，得到平均像素值及平均像素值變化量，接著以其像素飽和度之動態變化過濾雜訊，並藉由煙霧辨識參數，在彩煙球釋放的影片中辨識出煙霧前景；接著利用此前景像素的亮度分佈，進一步手動找出過濾雜訊的亮度門檻值，得到前景過濾畫面；最後以追蹤框鎖定最大煙霧前景，藉以準確地判斷煙霧的發生。

Fire detection has evolved with a long history. Those traditional heat and smoke detectors utilize, respectively, the heat and smoke produced by fire to react. While the flame detectors absorb IR/UV emitted from the flames and trigger if it gets to a certain level of intensity. Most of the above mentioned detectors are just elements of a complicated electrical alarm system. They can not be installed to operate by their own. Moreover, they tend to react late or erroneously by environmental interference because of the limitations to their operational principles. Most fires create a great amount of smoke in the initial stage. When it pervades all over the space, people can then be aware of the situation from a distance. Recently the cost of computer vision products has been decreasing in a great deal, which brings many applications in this field. It has been an obvious stream to use CCDs to directly detect the visible light of fires and smoke. This research will develop smoke detection software by a graphical programming language, which grabs the images of a workplace with smoke emitted. By transforming RGB color system to HSI system and filtering noises by changing pixels, the software is able to recognize the smoke foregrounds in each video frame. Next, the analysis of the intensity distribution of the foregrounds is performed subsequently. The manual finding of the features is to further eliminate the noises that are left during the previous filtration. Finally, the largest smoke foreground in the filtered image will be locked up by a tracking frame. Therefore, the software can accurately determine the real smoke.

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