A Method of Detecting Candidate Regions and Flames Based on Deep Learning Using Color-Based Pre-Processing

Abstract

Recently, object detection methods using deep learning have made significant progress in terms of accuracy and speed. However, the requirements of a system to provide real-time detection are somewhat high, and current methods are still insufficient to accurately detect important factors directly related to life and safety, such as fires. Therefore, this study attempted to improve the detection rate by supplementing the existing research to reduce the false detection rate of flame detection in fire and to reduce the number of candidate regions extracted in advance. To this end, pre-processing based on the HSV and YCbCr color models was performed to filter the flame area simply and strongly, and a selective search was used to detect a valid candidate region for the filtered image. In addition, for the detected candidate region, a deep learning-based convolutional neural network (CNN) was used to infer whether the object was a flame. As a result, it was found that the flame-detection accuracy of the model proposed in this study was 7% higher than that of the other models presented for comparison, and the recall rate was increased by 6%.