A Comprehensive Review of YOLO: From YOLOv1 and Beyond

Authors: Juan Terven, Diana Cordova-Esparza

arXiv: 2304.00501v2 - DOI (cs.CV)
31 pages, 15 figures, 4 tables, submitted to ACM Computing Surveys This version includes YOLO-NAS and a more detailed description of YOLOv5 and YOLOv8. It also adds three new diagrams for the architectures of YOLOv5, YOLOv8, and YOLO-NAS
License: CC BY 4.0

Abstract: YOLO has become a central real-time object detection system for robotics, driverless cars, and video monitoring applications. We present a comprehensive analysis of YOLO's evolution, examining the innovations and contributions in each iteration from the original YOLO to YOLOv8 and YOLO-NAS. We start by describing the standard metrics and postprocessing; then, we discuss the major changes in network architecture and training tricks for each model. Finally, we summarize the essential lessons from YOLO's development and provide a perspective on its future, highlighting potential research directions to enhance real-time object detection systems.

Submitted to arXiv on 02 Apr. 2023

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