You Only Look Once (YOLO) Real-time Object Detection Algorithms: A Systematic Literature Review

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dc.contributor.authorAsaju Christine Bukola,
dc.contributor.authorFlorence Funke Abiola
dc.date.accessioned2026-08-27T12:13:33Z
dc.date.available2026-08-27T12:13:33Z
dc.date.issued2025-07
dc.description.abstractReal-time object detection is essential in various computer vision applications, such as autonomous driving, surveillance, and robotics. Among deep learning based object detection frameworks, the You Only Look Once (YOLO) algorithm family stands out for its impressive speed, accuracy, and efficiency. This paper provides a systematic review of YOLO-based object detection models, examining their development over the years, enhancements, and practical uses. It explores the evolution of YOLO from its initial version to the latest advancements, focusing on key architectural changes, training methodologies, and performance optimizations. Furthermore, YOLO is compared with other leading object detection models, which outline its strengths and limitations. The review also explores domain-specific applications of YOLO, including medical imaging, autonomous navigation, and industrial automation. By consolidating findings from recent research, this study sheds light on existing challenges and potential future directions in real-time object detection using YOLO.
dc.identifier.citationChristine Bukola Asaju, FLourence Funke Abiola (2025)You Only Look Once (YOLO) Real-time Object Detection Algorithms: A Systematic Literature Review. July 2025
dc.identifier.urihttps://uilspace.unilorin.edu.ng/handle/123456789/18518
dc.language.isoen
dc.subjectYou Only Look Once
dc.subjectConvolutional Neural Network
dc.subjectDeep Learning
dc.subjectObject Detection Deep Learning
dc.subjectReal-time Detection
dc.subjectComputer Vision
dc.titleYou Only Look Once (YOLO) Real-time Object Detection Algorithms: A Systematic Literature Review

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