Image fire detection algorithms based on convolutional neural networks

Abstract

As a new fire detection technology, image fire detection has recently played a crucial role in reducing fire losses by alarming users early through early fire detection. Image fire detection is based on an algorithmic analysis of images. However, there is a lower accuracy, delayed detection, and a large amount of computation in common detection algorithms, including manually and machine automatically extracting image features. Therefore, novel image fire detection algorithms based on the advanced object detection CNN models of Faster-RCNN, R–FCN, SSD, and YOLO v3 are proposed in this paper. A comparison of the proposed and current algorithms reveals that the accuracy of fire detection algorithms based on object detection CNNs is higher than other algorithms. Especially, the average precision of the algorithm based on YOLO v3 reaches to 83.7%, which is higher than the other proposed algorithms. Besides, the YOLO v3 also has stronger robustness of detection performance, and its detection speed reaches 28 FPS, thereby satisfying the requirements of real-time detection.