Optimized Dual Fire Attention Network and Medium-Scale Fire Classification Benchmark
Author:
Affiliation:
1. Department of Software, Sejong University, Seoul, South Korea
2. School of Computer Science Engineering and Technology, Bennett University, Greater Noida, Uttar Pradesh, India
Funder
National Research Foundation of Korea (NRF) Grant through the Korean Government [Ministry of Science, ICT and Future Planning (MSIP)]
Institute of Information and Communications Technology Planning and Evaluation (IITP) Grant through the Korean Government
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
Computer Graphics and Computer-Aided Design,Software
Link
http://xplorestaging.ieee.org/ielx7/83/9626658/09898909.pdf?arnumber=9898909
Reference67 articles.
1. XNOR-Net: ImageNet classification using binary convolutional neural networks;rastegari;Proc Eur Conf Comput Vis,2016
2. Binaryconnect: Training deep neural networks with binary weights during propagations;courbariaux;Proc Adv Neural Inf Process Syst,2015
3. Early fire detection using convolutional neural networks during surveillance for effective disaster management
4. Wildfire Segmentation Using Deep Vision Transformers
5. Fire Detection using Transformer Network
Cited by 52 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献
1. Real-time verification of solar-powered forest fire detection system using ensemble learning;Expert Systems with Applications;2024-12
2. A modified vision transformer architecture with scratch learning capabilities for effective fire detection;Expert Systems with Applications;2024-10
3. Impact of Artificial Intelligence on the Global Economy and Technology Advancements;Advanced Technologies and Societal Change;2024-08-31
4. A Comparative Performance Evaluation of YOLO-Type Detectors on a New Open Fire and Smoke Dataset;Sensors;2024-08-29
5. Complex Scenes Fire Object Detection Based on Feature Fusion and Channel Attention;Arabian Journal for Science and Engineering;2024-08-25
1.学者识别学者识别
2.学术分析学术分析
3.人才评估人才评估
"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370
www.globalauthorid.com
TOP
Copyright © 2019-2024 北京同舟云网络信息技术有限公司 京公网安备11010802033243号 京ICP备18003416号-3