YOLOv1 to YOLOv11: A Comprehensive Survey

Over the past decade, the YOLO family has continuously redefined real-time object detection. This survey reviews YOLO versions from v1 through v11, analyzing architectural changes, benchmark results, and practical deployment trends.

Evolution Path

Each YOLO version balances speed and accuracy through architectural refinements, expanding from detection to segmentation, pose estimation, and tracking.

Architectural Milestones

The paper highlights major innovations such as feature pyramid networks, attention mechanisms, decoupled heads, and training optimizations that progressively improved real-time performance across diverse hardware platforms.

Provides a unified comparison across tasks, datasets, and deployment scenarios

Why This Survey Matters

Side-by-side comparison of YOLO variants
Coverage of real-world industrial and medical use cases
Benchmark summaries against competing detectors
Analysis of trade-offs across speed and accuracy
Future research directions for real-time vision

YOLOv1 to YOLOv11: A Comprehensive Survey