YOLOv1 to YOLOv11: A Comprehensive Survey of Real-Time Object Detection Innovations and Challenges

TL;DR

This paper reviews the evolution of YOLO object detection models from YOLOv1 to YOLOv11, highlighting innovations, extended capabilities, and real-world applications in real-time computer vision.

Contribution

It provides a comprehensive survey of architectural improvements, performance benchmarks, and new tasks supported by the YOLO family, along with analysis of future research directions.

Findings

YOLO models have progressively improved speed and accuracy.

Extended capabilities include instance segmentation and pose estimation.

YOLO models are widely applied in medical imaging and industrial automation.

Abstract

Over the past decade, object detection has advanced significantly, with the YOLO (You Only Look Once) family of models transforming the landscape of real-time vision applications through unified, end-to-end detection frameworks. From YOLOv1's pioneering regression-based detection to the latest YOLOv9, each version has systematically enhanced the balance between speed, accuracy, and deployment efficiency through continuous architectural and algorithmic advancements.. Beyond core object detection, modern YOLO architectures have expanded to support tasks such as instance segmentation, pose estimation, object tracking, and domain-specific applications including medical imaging and industrial automation. This paper offers a comprehensive review of the YOLO family, highlighting architectural innovations, performance benchmarks, extended capabilities, and real-world use cases. We critically analyze the evolution of YOLO models and discuss emerging research directions that extend their impact across diverse computer vision domains.