Flame Detection Based on Deep Learning Using HSV Color Model and Corner Detection Algorithm

Abstract

Recently, many image classification or object detection models that use deep learning techniques have been studied; however, in an actual performance evaluation, flame detection using these models may achieve low accuracy. Therefore, the flame detection method proposed in this study is image pre-processing with HSV color model conversion and the Harris corner detection algorithm. The application of the Harris corner detection method, which filters the output from the HSV color model, allows the corners to be detected around the flame owing to the rough texture characteristics of the flame image. These characteristics allow for the detection of a region of interest where multiple corners occur, and finally classify the flame status using deep learning-based convolutional neural network models. The flame detection of the proposed model in this study showed an accuracy of 97.5% and a precision of 97%.