{"title":"YOLOv5和YOLOv8在不同环境和推荐中的实时效率","authors":"Ali Hassan Sodhro, Sathwik Kannam, 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, Sathwik Kannam, 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}
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.
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.