2025
1.
Ranjan Sapkota; Marco Flores-Calero; Rizwan Qureshi; Chetan Badgujar; Upesh Nepal; Alwin Poulose; Peter Zeno; Uday Bhanu Prakash Vaddevolu; Sheheryar Khan; Maged Shoman; Hong Yan; Manoj Karkee
YOLO advances to its genesis: a decadal and comprehensive review of the You Only Look Once (YOLO) series Journal Article
In: Artificial Intelligence Review, vol. 58, no. 9, pp. 274, 2025, ISSN: 1573-7462.
Abstract | Links | BibTeX | Tags: Agriculture, Artificial intelligence, Autonomous vehicles, CNN, Computer vision, Deep learning, Healthcare and medical imaging, Industrial manufacturing, Real-time object detection, Surveillance, Traffic safety, YOLO, YOLO configurations, YOLOv1 to YOLOv12, You Only Look Once
@article{sapkota_yolo_2025,
title = {YOLO advances to its genesis: a decadal and comprehensive review of the You Only Look Once (YOLO) series},
author = {Ranjan Sapkota and Marco Flores-Calero and Rizwan Qureshi and Chetan Badgujar and Upesh Nepal and Alwin Poulose and Peter Zeno and Uday Bhanu Prakash Vaddevolu and Sheheryar Khan and Maged Shoman and Hong Yan and Manoj Karkee},
url = {https://doi.org/10.1007/s10462-025-11253-3},
doi = {10.1007/s10462-025-11253-3},
issn = {1573-7462},
year = {2025},
date = {2025-06-01},
urldate = {2025-06-01},
journal = {Artificial Intelligence Review},
volume = {58},
number = {9},
pages = {274},
abstract = {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.},
keywords = {Agriculture, Artificial intelligence, Autonomous vehicles, CNN, Computer vision, Deep learning, Healthcare and medical imaging, Industrial manufacturing, Real-time object detection, Surveillance, Traffic safety, YOLO, YOLO configurations, YOLOv1 to YOLOv12, You Only Look Once},
pubstate = {published},
tppubtype = {article}
}
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.
