Version 1
: Received: 19 June 2024 / Approved: 19 June 2024 / Online: 20 June 2024 (04:00:13 CEST)
Version 2
: Received: 23 June 2024 / Approved: 24 June 2024 / Online: 24 June 2024 (08:50:30 CEST)
How to cite:
Sapkota, R.; Qureshi, R.; Flores-Calero, M.; Badgujar, C.; Nepal, U.; Poulose, A.; Zeno, P.; Bhanu Prakash Vaddevolu, U.; Yan, H.; Karkee, M. YOLOv10 to Its Genesis: A Decadal and Comprehensive Review of The You Only Look Once Series. Preprints2024, 2024061366. https://doi.org/10.20944/preprints202406.1366.v2
Sapkota, R.; Qureshi, R.; Flores-Calero, M.; Badgujar, C.; Nepal, U.; Poulose, A.; Zeno, P.; Bhanu Prakash Vaddevolu, U.; Yan, H.; Karkee, M. YOLOv10 to Its Genesis: A Decadal and Comprehensive Review of The You Only Look Once Series. Preprints 2024, 2024061366. https://doi.org/10.20944/preprints202406.1366.v2
Sapkota, R.; Qureshi, R.; Flores-Calero, M.; Badgujar, C.; Nepal, U.; Poulose, A.; Zeno, P.; Bhanu Prakash Vaddevolu, U.; Yan, H.; Karkee, M. YOLOv10 to Its Genesis: A Decadal and Comprehensive Review of The You Only Look Once Series. Preprints2024, 2024061366. https://doi.org/10.20944/preprints202406.1366.v2
APA Style
Sapkota, R., Qureshi, R., Flores-Calero, M., Badgujar, C., Nepal, U., Poulose, A., Zeno, P., Bhanu Prakash Vaddevolu, U., Yan, H., & Karkee, M. (2024). YOLOv10 to Its Genesis: A Decadal and Comprehensive Review of The You Only Look Once Series. Preprints. https://doi.org/10.20944/preprints202406.1366.v2
Chicago/Turabian Style
Sapkota, R., Hong Yan and Manoj Karkee. 2024 "YOLOv10 to Its Genesis: A Decadal and Comprehensive Review of The You Only Look Once Series" Preprints. https://doi.org/10.20944/preprints202406.1366.v2
Abstract
This review systematically examines the progression of the You Only Look Once (YOLO) object detection algorithms from YOLOv1 to the recently unveiled YOLOv10. Employing a reverse chronological analysis, this study examines the advancements introduced by YOLO algorithms, beginning with YOLOv10 and progressing through YOLOv9, YOLOv8, and subsequent versions to explore each version's contributions to enhancing speed, accuracy, and computational efficiency in real-time object detection. The study highlights the transformative impact of YOLO across five critical application areas: automotive safety, healthcare, industrial manufacturing, surveillance, and agriculture. By detailing the incremental technological advancements that each iteration brought, this review not only chronicles the evolution of YOLO but also discusses the challenges and limitations observed in each earlier versions. The evolution signifies a path towards integrating YOLO with multimodal, context-aware, and General Artificial Intelligence (AGI) systems for the next YOLO decade, promising significant implications for future developments in AI-driven applications.
Keywords
You Only Look Once; YOLO; YOLOv10 to YOLOv1; CNN; Deep learning; Object detection; Artificial intelligence; Computer vision; Healthcare; Autonomous Vehicles; Industrial manufacturing; Surveillance; Agriculture; YOLOv10; YOLOv9; YOLOv8
Subject
Computer Science and Mathematics, Computer Science
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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