YOLOv1 to YOLOv10: The Fastest and Most Accurate Real-time Object Detection Systems

• Published in: APSIPA transactions on signal and information processing Vol. 13; no. 1
• Main Authors: Wang, Chien-Yao, Liao, Hong-Yuan Mark
• Format: Journal Article
• Language: English
• Published: Boston — Delft Now Publishers 01.01.2024; Now Publishers Inc
• Subjects: Applications and case studies; Computer Science; Computer Vision; Deep learning; Engineering; Literature reviews; Machine Learning; Model choice; Object and scene recognition; Object recognition; Real time; Technology
• ISSN: 2048-7703; 2048-7703
• DOI: 10.1561/116.20240058

This is a comprehensive review of the YOLO series of systems. Different from previous literature surveys, this review article reexamines the characteristics of the YOLO series from the latest technical point of view. At the same time, we also analyzed how the YOLO series continued to influence and promote realtime computer vision-related research and led to the subsequent development of computer vision and language models. We take a closer look at how the methods proposed by the YOLO series in the past ten years have affected the development of subsequent technologies and show the applications of YOLO in various fields. We hope this article can play a good guiding role in subsequent real-time computer vision development.