YOLOv5和YOLOv8在不同环境和推荐中的实时效率

IF 7.6 3区 计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS
Ali Hassan Sodhro, Sathwik Kannam, Michel Jensen
{"title":"YOLOv5和YOLOv8在不同环境和推荐中的实时效率","authors":"Ali Hassan Sodhro,&nbsp;Sathwik Kannam,&nbsp;Michel Jensen","doi":"10.1016/j.iot.2025.101707","DOIUrl":null,"url":null,"abstract":"<div><div>Intrusion Detection Systems (IDS) are essential for securing areas such as industrial and construction sites. However, when implementing IDS as a service, confidence scores (confidence) provided by YOLOv8 are the most reliable metric as compared to the YOLOv5 available to take appropriate actions to secure these sites and prevent intruders. However, prior research has focused on YOLO’s human detection capabilities (whether it can detect or not), neglecting real-time performance in IDS. To address this gap, we propose and present comparative analysis of YOLOv5 and YOLOv8 in a real-time across diverse environmental conditions (luminance, indoor/outdoor, simulated weather). Our findings reveal an average performance of YOLOv5 (outdoor: 90.5%, indoor: 79.1%), YOLOv8 (outdoor: 99.1%, Indoor: 77.2%) confidence in real-time, with a logarithmic relationship between luminance and confidence. Outdoor environments perform better then indoor for both YOLOv5 and YOLOv8, while adverse weather conditions significantly reduce YOLOv8’s effectiveness and increase the efficiency of YOLOv5. Therefore, this enables IDS integrators to adjust minimum confidence thresholds to minimize the risk of preventing potential intruders. However, the consistent and inconsistent confidence scores by both YOLOv8 and YOLOv5 respectively, and impact of weather remains inconclusive due to simulated fog.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":"33 ","pages":"Article 101707"},"PeriodicalIF":7.6000,"publicationDate":"2025-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Real-time efficiency of YOLOv5 and YOLOv8 in human intrusion detection across diverse environments and recommendation\",\"authors\":\"Ali Hassan Sodhro,&nbsp;Sathwik Kannam,&nbsp;Michel Jensen\",\"doi\":\"10.1016/j.iot.2025.101707\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>Intrusion Detection Systems (IDS) are essential for securing areas such as industrial and construction sites. However, when implementing IDS as a service, confidence scores (confidence) provided by YOLOv8 are the most reliable metric as compared to the YOLOv5 available to take appropriate actions to secure these sites and prevent intruders. However, prior research has focused on YOLO’s human detection capabilities (whether it can detect or not), neglecting real-time performance in IDS. To address this gap, we propose and present comparative analysis of YOLOv5 and YOLOv8 in a real-time across diverse environmental conditions (luminance, indoor/outdoor, simulated weather). Our findings reveal an average performance of YOLOv5 (outdoor: 90.5%, indoor: 79.1%), YOLOv8 (outdoor: 99.1%, Indoor: 77.2%) confidence in real-time, with a logarithmic relationship between luminance and confidence. Outdoor environments perform better then indoor for both YOLOv5 and YOLOv8, while adverse weather conditions significantly reduce YOLOv8’s effectiveness and increase the efficiency of YOLOv5. Therefore, this enables IDS integrators to adjust minimum confidence thresholds to minimize the risk of preventing potential intruders. However, the consistent and inconsistent confidence scores by both YOLOv8 and YOLOv5 respectively, and impact of weather remains inconclusive due to simulated fog.</div></div>\",\"PeriodicalId\":29968,\"journal\":{\"name\":\"Internet of Things\",\"volume\":\"33 \",\"pages\":\"Article 101707\"},\"PeriodicalIF\":7.6000,\"publicationDate\":\"2025-07-22\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Internet of Things\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2542660525002215\",\"RegionNum\":3,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"COMPUTER SCIENCE, INFORMATION SYSTEMS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Internet of Things","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2542660525002215","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
引用次数: 0

摘要

入侵检测系统(IDS)对于保护工业和建筑工地等区域至关重要。但是,在将IDS实现为服务时,与YOLOv5相比,YOLOv8提供的置信度分数(置信度)是最可靠的度量,可用于采取适当的行动来保护这些站点并防止入侵者。然而,先前的研究主要集中在YOLO的人类检测能力(是否可以检测)上,而忽略了IDS中的实时性能。为了解决这一差距,我们提出并展示了YOLOv5和YOLOv8在不同环境条件(亮度、室内/室外、模拟天气)下的实时比较分析。我们的研究结果显示,YOLOv5(室外:90.5%,室内:79.1%)和YOLOv8(室外:99.1%,室内:77.2%)实时置信度的平均性能与亮度和置信度之间呈对数关系。YOLOv5和YOLOv8的室外环境性能均优于室内环境,而恶劣的天气条件显著降低了YOLOv8的效能,提高了YOLOv5的效率。因此,这使IDS集成商能够调整最小置信度阈值,以最小化防止潜在入侵者的风险。然而,由于模拟的雾,YOLOv8和YOLOv5的一致和不一致的置信度得分以及天气的影响仍然是不确定的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Real-time efficiency of YOLOv5 and YOLOv8 in human intrusion detection across diverse environments and recommendation
Intrusion Detection Systems (IDS) are essential for securing areas such as industrial and construction sites. However, when implementing IDS as a service, confidence scores (confidence) provided by YOLOv8 are the most reliable metric as compared to the YOLOv5 available to take appropriate actions to secure these sites and prevent intruders. However, prior research has focused on YOLO’s human detection capabilities (whether it can detect or not), neglecting real-time performance in IDS. To address this gap, we propose and present comparative analysis of YOLOv5 and YOLOv8 in a real-time across diverse environmental conditions (luminance, indoor/outdoor, simulated weather). Our findings reveal an average performance of YOLOv5 (outdoor: 90.5%, indoor: 79.1%), YOLOv8 (outdoor: 99.1%, Indoor: 77.2%) confidence in real-time, with a logarithmic relationship between luminance and confidence. Outdoor environments perform better then indoor for both YOLOv5 and YOLOv8, while adverse weather conditions significantly reduce YOLOv8’s effectiveness and increase the efficiency of YOLOv5. Therefore, this enables IDS integrators to adjust minimum confidence thresholds to minimize the risk of preventing potential intruders. However, the consistent and inconsistent confidence scores by both YOLOv8 and YOLOv5 respectively, and impact of weather remains inconclusive due to simulated fog.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Internet of Things
Internet of Things Multiple-
CiteScore
3.60
自引率
5.10%
发文量
115
审稿时长
37 days
期刊介绍: Internet of Things; Engineering Cyber Physical Human Systems is a comprehensive journal encouraging cross collaboration between researchers, engineers and practitioners in the field of IoT & Cyber Physical Human Systems. The journal offers a unique platform to exchange scientific information on the entire breadth of technology, science, and societal applications of the IoT. The journal will place a high priority on timely publication, and provide a home for high quality. Furthermore, IOT is interested in publishing topical Special Issues on any aspect of IOT.
×
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:604180095
Book学术官方微信