You Only Look Once (YOLO) Real-time Object Detection Algorithms: A Systematic Literature Review
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Date
2025-07
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Abstract
Real-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.
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Keywords
You Only Look Once, Convolutional Neural Network, Deep Learning, Object Detection Deep Learning, Real-time Detection, Computer Vision
Citation
Christine Bukola Asaju, FLourence Funke Abiola (2025)You Only Look Once (YOLO) Real-time Object Detection Algorithms: A Systematic Literature Review. July 2025