A Video-Based Fire Detection Using Deep Learning Models

• Main Authors: Byoungjun Kim, Joonwhoan Lee
• Format: Article
• Language: English
• Published: MDPI AG 2019-07-01
• Series: Applied Sciences
• Subjects: deep learning; fire detection; Faster R-CNN; spatiotemporal feature; LSTM; majority voting; dynamic fire behavior
• Online Access: https://www.mdpi.com/2076-3417/9/14/2862

Fire is an abnormal event which can cause significant damage to lives and property. In this paper, we propose a deep learning-based fire detection method using a video sequence, which imitates the human fire detection process. The proposed method uses Faster Region-based Convolutional Neural Network (R-CNN) to detect the suspected regions of fire (SRoFs) and of non-fire based on their spatial features. Then, the summarized features within the bounding boxes in successive frames are accumulated by Long Short-Term Memory (LSTM) to classify whether there is a fire or not in a short-term period. The decisions for successive short-term periods are then combined in the majority voting for the final decision in a long-term period. In addition, the areas of both flame and smoke are calculated and their temporal changes are reported to interpret the dynamic fire behavior with the final fire decision. Experiments show that the proposed long-term video-based method can successfully improve the fire detection accuracy compared with the still image-based or short-term video-based method by reducing both the false detections and the misdetections.