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http://dx.doi.org/10.7471/ikeee.2018.22.4.1079

A Study on Flame Detection using Faster R-CNN and Image Augmentation Techniques  

Kim, Jae-Jung (Graduate School of Disaster Prevention, Kangwon National University)
Ryu, Jin-Kyu (Graduate School of Disaster Prevention, Kangwon National University)
Kwak, Dong-Kurl (Graduate School of Disaster Prevention, Kangwon National University)
Byun, Sun-Joon (Thermal-Hydraulic Design Team, KEPCO Nuclear Fuel)
Publication Information
Journal of IKEEE / v.22, no.4, 2018 , pp. 1079-1087 More about this Journal
Abstract
Recently, computer vision field based deep learning artificial intelligence has become a hot topic among various image analysis boundaries. In this study, flames are detected in fire images using the Faster R-CNN algorithm, which is used to detect objects within the image, among various image recognition algorithms based on deep learning. In order to improve fire detection accuracy through a small amount of data sets in the learning process, we use image augmentation techniques, and learn image augmentation by dividing into 6 types and compare accuracy, precision and detection rate. As a result, the detection rate increases as the type of image augmentation increases. However, as with the general accuracy and detection rate of other object detection models, the false detection rate is also increased from 10% to 30%.
Keywords
Artificial intelligency; Deep leaning; Object detection; Faster R-CNN; Image augmentation;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
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