@article{ref1,
title="FlameNet: a lightweight convolutional neural network for flame detection and localisation",
journal="International journal of vehicle design",
year="2023",
author="Hu, Xing and Li, Mei and Zhang, Dawei",
volume="91",
number="1/2/3",
pages="87-106",
abstract="Accurately and efficiently detecting and localising the flame is critical for preventing fire disasters. However, most high-performance deep models require high hardware resources, making them hard to deploy on edge or mobile intelligent devices. This paper proposes a lightweight deep convolutional neural network, called FlameNet, for flame detection and localisation. The proposed FlameNet is derived from YOLOv4 with the following modifications: First, MobileNetV2 replaces the CSPDarknet53 as the new backbone network of YOLOv4; Second, the Coordinate Attention module is added to the inverted residual linear bottleneck of MobileNetV2; Third, the Depthwise Separable Convolutions is used in PANet of YOLOv4. There is no large-scale public dataset available, and we produced a real-world flame (RWF) dataset containing 13,129 images. Qualitative and quantitative analysis and comparisons of experimental results demonstrate that the accuracy and efficiency of the proposed FlameNet for flame detection out-perform the original YOLOv4 and the other relative models.<p /> <p>Language: en</p>",
language="en",
issn="0143-3369",
doi="10.1504/IJVD.2023.131044",
url="http://dx.doi.org/10.1504/IJVD.2023.131044"
}