Abstract
The YOLO (You Only Look Once) series has been a leading framework in real-time object detection, consistently improving the balance between speed and accuracy. However, integrating attention mechanisms into YOLO has been challenging due to their high computational overhead. YOLOv12 introduces a novel approach that successfully incorporates attention-based enhancements while preserving real-time performance. This paper provides a comprehensive review of YOLOv12's architectural innovations, including Area Attention for computationally efficient self-attention, Residual Efficient Layer Aggregation Networks for improved feature aggregation, and FlashAttention for optimized memory access. Additionally, we benchmark YOLOv12 against prior YOLO versions and competing object detectors, analyzing its improvements in accuracy, inference speed, and computational efficiency. Through this analysis, we demonstrate how YOLOv12 advances real-time object detection by refining the latency-accuracy trade-off and optimizing computational resources.
Abstract (translated)
YOLO（You Only Look Once）系列一直是实时目标检测领域的领军框架，持续优化速度与准确性的平衡。然而，由于注意力机制的高计算开销，将其集成到YOLO中一直颇具挑战性。YOLOv12则引入了一种新颖的方法，在保持实时性能的同时成功地集成了基于注意机制的改进。本文全面回顾了YOLOv12的架构创新，包括用于高效自注意力的区域注意（Area Attention）、用于改进特征聚合的残差高效层聚合网络（Residual Efficient Layer Aggregation Networks）以及优化内存访问的FlashAttention。此外，我们还对YOLOv12与先前版本的YOLO以及其他竞争性目标检测器进行了基准测试，分析了其在准确性、推理速度和计算效率方面的改进。通过这些分析，我们展示了如何通过细化延迟-准确性的权衡并优化计算资源，使YOLOv12推动了实时目标检测技术的发展。
URL
https://arxiv.org/abs/2504.11995
