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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Bibliographic Details
Published in:Computers (Basel) Vol. 13; no. 12; p. 336
Main Authors: Ali, Momina Liaqat, Zhang, Zhou
Format: Journal Article
Language:English
Published: Basel MDPI AG 01.12.2024
Subjects:
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 Access:Get full text
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Summary: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 YOLO variants, highlighting key architectural improvements, performance benchmarks, and applications in domains such as healthcare, autonomous vehicles, and robotics. It also evaluates the framework’s strengths and limitations in practical scenarios, addressing challenges like small object detection, environmental variability, and computational constraints. By synthesizing findings from recent research, this work identifies critical gaps in the literature and outlines future directions to enhance YOLO’s adaptability, robustness, and integration into emerging technologies. This review provides researchers and practitioners with valuable insights to drive innovation in object detection and related applications.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
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ISSN:2073-431X
2073-431X
DOI:10.3390/computers13120336