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Deep Learning and Color Histogram based Fire and Smoke Detection Research KCI 등재

Yeunghak Lee, Jaechang Shim

국제인공지능학회(구 한국인터넷방송통신학회) The International Journal of Advanced Smart Convergence Volume 8 Number 2 2019.06 pp.116-125

※ 원문제공기관과의 협약기간이 종료되어 열람이 제한될 수 있습니다.

The fire should extinguish as soon as possible because it causes economic loss and loses precious life. In this study, we propose a new atypical fire and smoke detection algorithm using deep learning and color histogram of fire and smoke. First, input frame images obtain from the ONVIF surveillance camera mounted in factory search motion candidate frame by motion detection algorithm and mean square error (MSE). Second deep learning (Faster R-CNN) is used to extract the fire and smoke candidate area of motion frame. Third, we apply a novel algorithm to detect the fire and smoke using color histogram algorithm with local area motion, similarity, and MSE. In this study, we developed a novel fire and smoke detection algorithm applied the local motion and color histogram method. Experimental results show that the surveillance camera with the proposed algorithm showed good fire and smoke detection results with very few false positives.

 
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