The YOLO Framework: A Comprehensive Review of Evolution, Applications, and Benchmarks in Object Detection

This paper provides a comprehensive review of the YOLO (You Only Look Once) framework up to its latest version, YOLO 11. As a state-of-the-art model for object detection, YOLO has revolutionized the field by achieving an optimal balance between speed and accuracy. The review traces the evolution of...

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Veröffentlicht in:	Computers (Basel) Jg. 13; H. 12; S. 336
Hauptverfasser:	Ali, Momina Liaqat, Zhang, Zhou
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Basel MDPI AG 01.12.2024
Schlagworte:	Accuracy Algorithms Analysis Artificial intelligence Autonomous vehicles Benchmarks Classification Computer vision deep neural network Efficiency Ethics Innovations Machine learning Machine vision Medical imaging Neural networks Object recognition Object recognition (Computers) Optimization techniques Pattern recognition performance evaluation Privacy Robotics Sensors single stage detection State-of-the-art reviews Surveillance YOLO YOLOv10 YOLOv11 United States
ISSN:	2073-431X, 2073-431X
Online-Zugang:	Volltext
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