【摘要】：針對煤礦井下環(huán)境特點,提出了基于數字圖像處理和支持向量機的煤礦外因火災早期探測方法。該方法根據火災初期的變化特征,用圖像處理方法提取溫度變化率、面積增長率、火焰閃爍頻率和整體穩(wěn)定性等特征值,并將其作為輸入信號,利用支持向量機進行數據融合后實現火災探測。實驗結果表明,該方法能夠對煤礦井下高�；鹪春透蓴_信號進行分類識別,具有探測率高、誤判率低、實時性好、魯棒性強的特點。
[Abstract]:According to the characteristics of underground coal mine environment, this paper presents an early detection method of coal mine external fire based on digital image processing and support vector machine. According to the characteristics of the initial fire, the image processing method is used to extract the characteristic values of temperature change rate, area growth rate, flame flicker frequency and overall stability, and take them as input signals. Support vector machine (SVM) is used to realize fire detection after data fusion. The experimental results show that this method can be used to classify and identify high risk fire sources and interference signals in coal mines, and has the characteristics of high detection rate, low error rate, good real-time performance and strong robustness.
【作者單位】： 西安科技大學電氣與控制工程學院;
【基金】：國家自然科學基金項目(51277149) 陜西省教育廳專項項目(14JK1467) 西安科技大學博士啟動基金項目(2014QDJ010)
【分類號】：TD752.3
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本文編號：2358418