YOLO advances to its genesis: a decadal and comprehensive review of the You Only Look Once (YOLO) series

This review systematically examines the progression of the You Only Look Once (YOLO) object detection algorithms from YOLOv1 to the recently unveiled YOLOv12. Employing a reverse chronological analysis, this study examines the advancements introduced by YOLO algorithms, beginning with YOLOv12 and pr...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:The Artificial intelligence review Jg. 58; H. 9; S. 274
Hauptverfasser: Sapkota, Ranjan, Flores-Calero, Marco, Qureshi, Rizwan, Badgujar, Chetan, Nepal, Upesh, Poulose, Alwin, Zeno, Peter, Vaddevolu, Uday Bhanu Prakash, Khan, Sheheryar, Shoman, Maged, Yan, Hong, Karkee, Manoj
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Dordrecht Springer Netherlands 11.06.2025
Springer Nature B.V
Schlagworte:
Agriculture
Algorithms
Alternative approaches
Artificial Intelligence
Computer Science
Health care
Health services
Intelligence
Manufacturing
Medical imaging
Object recognition
Real time
Surveillance
Technological change
Traffic accidents & safety
Computer vision
CNN
Healthcare and medical imaging
YOLOv1 to YOLOv12
Real-time object detection
YOLO
Deep learning
Industrial manufacturing
Surveillance
Traffic safety
Agriculture
You Only Look Once
Artificial intelligence
YOLO configurations
Autonomous vehicles
ISSN:1573-7462, 0269-2821, 1573-7462
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:This review systematically examines the progression of the You Only Look Once (YOLO) object detection algorithms from YOLOv1 to the recently unveiled YOLOv12. Employing a reverse chronological analysis, this study examines the advancements introduced by YOLO algorithms, beginning with YOLOv12 and progressing through YOLO11 (or YOLOv11), YOLOv10, YOLOv9, YOLOv8, and subsequent versions to explore each version’s contributions to enhancing speed, detection accuracy, and computational efficiency in real-time object detection. Additionally, this study reviews the alternative versions derived from YOLO architectural advancements of YOLO-NAS, YOLO-X, YOLO-R, DAMO-YOLO, and Gold-YOLO. Moreover, the study highlights the transformative impact of YOLO models across five critical application areas: autonomous vehicles and traffic safety, healthcare and medical imaging, industrial manufacturing, surveillance and security, and agriculture. By detailing the incremental technological advancements in subsequent YOLO versions, this review chronicles the evolution of YOLO, and discusses the challenges and limitations in each of the earlier versions. The evolution signifies a path towards integrating YOLO with multimodal, context-aware, and Artificial General Intelligence (AGI) systems for the next YOLO decade, promising significant implications for future developments in AI-driven applications.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1573-7462
0269-2821
1573-7462
DOI:10.1007/s10462-025-11253-3