YOLOv12: Attention-Centric Real-Time Object Detectors
Yunjie Tian, Qixiang Ye, David Doermann·February 18, 2025
Summary
YOLOv12, an attention-focused real-time object detector, surpasses popular models in accuracy with competitive speed, outperforming YOLOv10/N, YOLOv11/N, and RT-DETR/R18. It introduces the area attention module (A2), residual efficient layer aggregation networks (R-ELAN), and architectural improvements for enhanced performance. YOLOv12 achieves significant latency-accuracy and FLOPs-accuracy trade-offs, breaking CNN dominance in YOLO systems with fast inference speed and higher detection accuracy.
Introduction
Background
Overview of YOLO series
Importance of real-time object detection
Objective
To introduce YOLOv12, a model that surpasses popular object detectors in accuracy while maintaining competitive speed
Method
Architecture Innovations
Area Attention Module (A2)
Description of A2
How A2 improves detection accuracy
Residual Efficient Layer Aggregation Networks (R-ELAN)
Explanation of R-ELAN
Benefits of R-ELAN in terms of efficiency and performance
Architectural Improvements
Overview of additional enhancements in YOLOv12
Performance Metrics
Latency-accuracy trade-offs
FLOPs-accuracy trade-offs
Comparison with Competitors
YOLOv10/N, YOLOv11/N, and RT-DETR/R18
Results
Detection Accuracy
Quantitative analysis of YOLOv12's performance
Inference Speed
Comparison of inference times with other models
Breakthrough in CNN Dominance
Explanation of how YOLOv12 challenges the dominance of CNNs in YOLO systems
Conclusion
Summary of YOLOv12's Advantages
Recap of YOLOv12's key features and benefits
Future Directions
Potential areas for further research and development in YOLOv12 and related object detection models
Basic info
papers
computer vision and pattern recognition
artificial intelligence
Advanced features
Insights
What is YOLOv12 and how does it improve upon previous models like YOLOv10/N and YOLOv11/N?
How does YOLOv12 achieve a balance between latency, accuracy, and FLOPs, and what impact does this have on its performance compared to CNN-based systems?
What are the key components introduced in YOLOv12 that contribute to its enhanced performance?
What are the specific architectural improvements made in YOLOv12 that enable it to surpass other real-time object detection models in terms of detection accuracy and inference speed?