A Comprehensive Review of YOLO Architectures in Computer Vision: From YOLOv1 to YOLOv8 and YOLO-NAS

By Juan R. Terven, Diana-Margarita Córdova-Esparza, Julio-Alejandro Romero-González. Machine Learning and Knowledge Extraction, 2023

2,722 citations recorded by the source corpus.

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.

Subfields: Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/913d86a84afae61b51281a1bce2edbd72b7c7acb