Fast-COS提升自动驾驶目标检测速度与精度，适配边缘设备实时运行。
Fast-COS: A Fast One-Stage Object Detector Based on Reparameterized Attention Vision Transformer for Autonomous Driving
- 采用重参数化注意力视觉变压器（RAViT）提升计算效率与特征提取能力。
- 在BDD100K数据集上达57.2% AP50，GPU推理速度比YOLOF快75.9%。
- 专为资源受限的自动驾驶场景设计，适合部署于边缘和移动端。
感知系统是自动驾驶安全的关键。驾驶场景感知本质上是目标检测任务，需兼顾准确率与处理速度。现有方法多关注精度，忽视了有限算力下的实时性。本文提出Fast-COS，一种专为驾驶场景设计的单阶段检测框架。研究从骨干网络的宏观与微观结构出发，提出重参数化注意力视觉变压器（RAViT），融合重参数化多尺度深度卷积（RepMSDW）与重参数化自注意力（RepSA），显著提升计算效率与特征表达。在ImageNet-1K上达到81.4% Top-1准确率，相比ResNet、FastViT等模型有明显吞吐优势。结合RepMSDW构建的RepFPN实现快速多尺度特征融合。Fast-COS在BDD100K上取得57.2% AP50，TJU-DHD数据集上达80.0%。相比FCOS、YOLOF、RetinaNet，GPU推理速度提升75.9%，边缘设备吞吐量提高1.38倍，验证其在资源受限环境下的高可扩展性与可靠性。
原文摘要 · Abstract (English)
The perception system is a a critical role of an autonomous driving system for ensuring safety. The driving scene perception system fundamentally represents an object detection task that requires achieving a balance between accuracy and processing speed. Many contemporary methods focus on improving detection accuracy but often overlook the importance of real-time detection capabilities when computational resources are limited. Thus, it is vital to investigate efficient object detection strategies for driving scenes. This paper introduces Fast-COS, a novel single-stage object detection framework crafted specifically for driving scene applications. The research initiates with an analysis of the backbone, considering both macro and micro architectural designs, yielding the Reparameterized Attention Vision Transformer (RAViT). RAViT utilizes Reparameterized Multi-Scale Depth-Wise Convolution (RepMSDW) and Reparameterized Self-Attention (RepSA) to enhance computational efficiency and feature extraction. In extensive tests across GPU, edge, and mobile platforms, RAViT achieves 81.4% Top-1 accuracy on the ImageNet-1K dataset, demonstrating significant throughput improvements over comparable backbone models such as ResNet, FastViT, RepViT, and EfficientFormer. Additionally, integrating RepMSDW into a feature pyramid network forms RepFPN, enabling fast and multi-scale feature fusion. Fast-COS enhances object detection in driving scenes, attaining an AP50 score of 57.2% on the BDD100K dataset and 80.0% on the TJU-DHD Traffic dataset. It surpasses leading models in efficiency, delivering up to 75.9% faster GPU inference and 1.38 higher throughput on edge devices compared to FCOS, YOLOF, and RetinaNet. These findings establish Fast-COS as a highly scalable and reliable solution suitable for real-time applications, especially in resource-limited environments like autonomous driving systems
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