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...

Celý popis

Uložené v:
Podrobná bibliografia
Vydané v:Computers (Basel) Ročník 13; číslo 12; s. 336
Hlavní autori: Ali, Momina Liaqat, Zhang, Zhou
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Basel MDPI AG 01.12.2024
Predmet:
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
On-line prístup:Získať plný text
Tagy: Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
Popis
Shrnutí: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.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:2073-431X
2073-431X
DOI:10.3390/computers13120336