智慧城市中的视觉智能:物联网环境中用于火灾探测的轻量级深度学习模型

IF 7 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC
Muhammad Nadeem, Naqqash Dilshad, N. Alghamdi, L. Dang, Hyoung-Kyu Song, Junyoung Nam, Hyeonjoon Moon
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引用次数: 1

摘要

在早期阶段识别火灾并阻止其造成社会经济和环境灾难仍然是一项艰巨的任务。尽管有令人信服的网络可用性,但仍需要为资源受限设备开发轻量级网络,而不是智能城市环境中的实时火灾探测。为了克服这一缺点,我们提出了一种新的高效轻量级网络,称为FlameNet,用于智能城市环境中的火灾探测。我们提出的网络通过两个主要步骤工作:首先,它使用FlameNet检测火灾;然后,警报被启动并指向消防、医疗和救援部门。此外,我们整合了MSA模块,以有效地优先考虑和增强与火灾相关的突出特征,从而有效地进行火灾探测。新开发的点火火焰数据集用于对几个卷积神经网络(CNN)模型进行彻底分析。此外,所提出的FlameNet对火灾的探测准确率达到99.40%。通过对模型精度、尺寸和处理时间等多因素的实证研究和分析,证明了该模型适用于火灾探测。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Visual Intelligence in Smart Cities: A Lightweight Deep Learning Model for Fire Detection in an IoT Environment
The recognition of fire at its early stages and stopping it from causing socioeconomic and environmental disasters remains a demanding task. Despite the availability of convincing networks, there is a need to develop a lightweight network for resource-constraint devices rather than real-time fire detection in smart city contexts. To overcome this shortcoming, we presented a novel efficient lightweight network called FlameNet for fire detection in a smart city environment. Our proposed network works via two main steps: first, it detects the fire using the FlameNet; then, an alert is initiated and directed to the fire, medical, and rescue departments. Furthermore, we incorporate the MSA module to efficiently prioritize and enhance relevant fire-related prominent features for effective fire detection. The newly developed Ignited-Flames dataset is utilized to undertake a thorough analysis of several convolutional neural network (CNN) models. Additionally, the proposed FlameNet achieves 99.40% accuracy for fire detection. The empirical findings and analysis of multiple factors such as model accuracy, size, and processing time prove that the suggested model is suitable for fire detection.
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来源期刊
Smart Cities
Smart Cities Multiple-
CiteScore
11.20
自引率
6.20%
发文量
0
审稿时长
11 weeks
期刊介绍: Smart Cities (ISSN 2624-6511) provides an advanced forum for the dissemination of information on the science and technology of smart cities, publishing reviews, regular research papers (articles) and communications in all areas of research concerning smart cities. Our aim is to encourage scientists to publish their experimental and theoretical results in as much detail as possible, with no restriction on the maximum length of the papers published so that all experimental results can be reproduced.
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