# YOLOv12: A Breakdown of the Key Architectural Features

arXiv: https://arxiv.org/abs/2502.14740 · HTML: https://mlsanity.com/paper/2502.14740/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Feb 2025.

## Built on by (most cited first, 3 of 3)

- [Representation Shift: Unifying Token Compression with FlashAttention](https://mlsanity.com/paper/2508.00367/) (2025)
- [TSBOW -- Traffic Surveillance Benchmark for Occluded Vehicles Under Various Weather Conditions](https://mlsanity.com/paper/2602.05414/) (2026)
- [An AI-Powered Autonomous Underwater System for Sea Exploration and Scientific Research](https://mlsanity.com/paper/2512.07652/) (2025)

## Abstract (arXiv)

This paper presents an architectural analysis of YOLOv12, a significant advancement in single-stage, real-time object detection building upon the strengths of its predecessors while introducing key improvements. The model incorporates an optimised backbone (R-ELAN), 7x7 separable convolutions, and FlashAttention-driven area-based attention, improving feature extraction, enhanced efficiency, and robust detections. With multiple model variants, similar to its predecessors, YOLOv12 offers scalable solutions for both latency-sensitive and high-accuracy applications. Experimental results manifest consistent gains in mean average precision (mAP) and inference speed, making YOLOv12 a compelling choice for applications in autonomous systems, security, and real-time analytics. By achieving an optimal balance between computational efficiency and performance, YOLOv12 sets a new benchmark for real-time computer vision, facilitating deployment across diverse hardware platforms, from edge devices to high-performance clusters.