A Comprehensive Review of YOLO Architectures in Computer Vision: From YOLOv1 to YOLOv8 and YOLO-NAS
Instituto Politécnico Nacional · Autonomous University of Queretaro
Indexed incrossrefdoaj
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 up to YOLOv8, YOLO-NAS, and YOLO with transformers. 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.
Citation impact
2,489
total citations
- FWCI
- 282.70
- Percentile
- 100%
- References
- 107
Citations per year
Authors
3Topics & keywords
Topics
Keywords
- Computer science
- Architecture
- Artificial intelligence
- Object detection
- Robotics
- Systems engineering
- Human–computer interaction
- Engineering
UN Sustainable Development Goals
- Industry, innovation and infrastructure
No related works found for this paper.