AFAR-YOLO: An Adaptive YOLO Object Detection Framework

Ainal Irham, Kurniadi, Khoirinisa Yuliandari, Farhan Mozart Aditya Fahreza, Daffa Riyadi, A. M. Shiddiqi
{"title":"AFAR-YOLO: An Adaptive YOLO Object Detection Framework","authors":"Ainal Irham, Kurniadi, Khoirinisa Yuliandari, Farhan Mozart Aditya Fahreza, Daffa Riyadi, A. M. Shiddiqi","doi":"10.1109/ICETSIS61505.2024.10459422","DOIUrl":null,"url":null,"abstract":"This study focuses on developing an advanced early warning system utilizing YOLOv5 to detect objects indicative of potential fire hazards. This research is motivated by the fact that continuous monitoring is impractical, especially in high-risk and inaccessible areas. We introduce an innovative approach: adaptive YOLO for object detection to enhance early fire detection capabilities in these challenging environments. The main contribution of this research is the development of adaptive frames per second (FPS) resolution in YOLO object detection. We found that implementing adaptive FPS alone does not significantly impact the efficiency of CPU and RAM resources in the tested devices. However, when adaptive FPS is combined with adaptive resolution, resource usage is significantly reduced–specifically, a 33% decrease in CPU usage and a 0.5-1% (200-400 MB) reduction in RAM usage. These efficiency gains are important in enhancing safety in the industrial sector.","PeriodicalId":518932,"journal":{"name":"2024 ASU International Conference in Emerging Technologies for Sustainability and Intelligent Systems (ICETSIS)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2024-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2024 ASU International Conference in Emerging Technologies for Sustainability and Intelligent Systems (ICETSIS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICETSIS61505.2024.10459422","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

This study focuses on developing an advanced early warning system utilizing YOLOv5 to detect objects indicative of potential fire hazards. This research is motivated by the fact that continuous monitoring is impractical, especially in high-risk and inaccessible areas. We introduce an innovative approach: adaptive YOLO for object detection to enhance early fire detection capabilities in these challenging environments. The main contribution of this research is the development of adaptive frames per second (FPS) resolution in YOLO object detection. We found that implementing adaptive FPS alone does not significantly impact the efficiency of CPU and RAM resources in the tested devices. However, when adaptive FPS is combined with adaptive resolution, resource usage is significantly reduced–specifically, a 33% decrease in CPU usage and a 0.5-1% (200-400 MB) reduction in RAM usage. These efficiency gains are important in enhancing safety in the industrial sector.
AFAR-YOLO:自适应 YOLO 物体检测框架
本研究的重点是利用 YOLOv5 开发一种先进的预警系统,以探测显示潜在火灾危险的物体。之所以开展这项研究,是因为持续监测是不切实际的,尤其是在高风险和交通不便的地区。我们引入了一种创新方法:用于物体检测的自适应 YOLO,以增强在这些具有挑战性的环境中的早期火灾检测能力。这项研究的主要贡献是在 YOLO 目标检测中开发了自适应每秒帧数(FPS)分辨率。我们发现,单独实施自适应 FPS 不会对测试设备的 CPU 和 RAM 资源效率产生重大影响。但是,当自适应 FPS 与自适应分辨率相结合时,资源使用率会明显降低,特别是 CPU 使用率降低 33%,RAM 使用率降低 0.5-1%(200-400 MB)。这些效率的提高对于增强工业领域的安全性非常重要。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信