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http://dx.doi.org/10.7471/ikeee.2019.23.1.242

Fase Positive Fire Detection Improvement Research using the Frame Similarity Principal based on Deep Learning  

Lee, Yeung-Hak (Dept. of Computer Engineering, Andong National University)
Shim, Jae-Chnag (Dept. of Computer Engineering, Andong National University)
Publication Information
Journal of IKEEE / v.23, no.1, 2019 , pp. 242-248 More about this Journal
Abstract
Fire flame and smoke detection algorithm studies are challenging task in computer vision due to the variety of shapes, rapid spread and colors. The performance of a typical sensor based fire detection system is largely limited by environmental factors (indoor and fire locations). To solve this problem, a deep learning method is applied. Because it extracts the feature of the object using several methods, so that if a similar shape exists in the frame, it can be detected as false postive. This study proposes a new algorithm to reduce false positives by using frame similarity before using deep learning to decrease the false detection rate. Experimental results show that the fire detection performance is maintained and the false positives are reduced by applying the proposed method. It is confirmed that the proposed method has excellent false detection performance.
Keywords
deep learning; faster r-cnn; fire detection; smoke detection; ssim;
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