A Decade of You Only Look Once (YOLO) for Object Detection: A Review
It provides a comprehensive overview for researchers and practitioners in computer vision, but is incremental as it synthesizes existing knowledge without introducing new methods.
This review paper examines the evolution of the YOLO framework over ten years, highlighting its development from a simple detector to a versatile family of architectures for real-time object detection, and discusses its applications, evaluation, and future directions.
This review marks the tenth anniversary of You Only Look Once (YOLO), one of the most influential frameworks in real-time object detection. Over the past decade, YOLO has evolved from a streamlined detector into a diverse family of architectures characterized by efficient design, modular scalability, and cross-domain adaptability. The paper presents a technical overview of the main versions (from YOLOv1 to YOLOv13), highlights key architectural trends, and surveys the principal application areas in which YOLO has been adopted. It also addresses evaluation practices, ethical considerations, and potential future directions for the framework's continued development. The analysis aims to provide a comprehensive and critical perspective on YOLO's trajectory and ongoing transformation.