# A Decade of You Only Look Once (YOLO) for Object Detection: A Review

Source: [A Decade of You Only Look Once (YOLO) for Object Detection: A Review](<https://www.sota2.com/research/paper/68fa7a661e209131cea82ec9>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-11-22T05:01:45.247Z

Authors: Leo Thomas Ramos, Angel D. Sappa

Published: 2025-04-24T00:06:08.000Z

## Abstract

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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2504.18586>)

[PDF](<https://arxiv.org/pdf/2504.18586.pdf>)