综述自动驾驶用单阶段检测器，分析速度与精度的权衡。
One-Stage Object Detectors in Autonomous Driving
- 梳理YOLO、RetinaNet、FCOS等主流单阶段检测架构
- 对比不同模型在真实驾驶场景中的速度与准确率表现
- 适合关注自动驾驶感知系统优化的研究者参考
自动驾驶车辆依赖快速可靠的感知系统，在实时检测周围车辆、行人、骑车人、交通标志及其他道路物体。本文并非提出新检测系统，而是对自动驾驶领域中单阶段目标检测器进行系统性综述与分析。文章回顾了主要检测器的发展历程，包括YOLOv1、SSD、RetinaNet、EfficientDet、无锚框检测器如FCOS和CenterNet，以及近期实时模型YOLOv10。通过设计选择、特征融合策略、损失函数、部署权衡及基准性能报告，对比各类架构。同时总结常用自动驾驶数据集、评估指标、开放挑战与未来研究方向。整体表明，单阶段检测器在速度、精度、效率与鲁棒性间取得平衡，但仍存在基准结果与实际可靠自动驾驶性能之间的差距。
原文摘要 · Abstract (English)
Autonomous vehicles depend on fast and reliable perception systems to detect surrounding vehicles, pedestrians, cyclists, traffic signs, and other road objects in real time. This paper presents a comprehensive survey and analysis of one-stage object detectors for autonomous driving rather than an implementation of a new detection system. The survey reviews the evolution of major one-stage detectors, including YOLOv1, SSD, RetinaNet, EfficientDet, anchor-free detectors such as FCOS and CenterNet, and recent real-time models such as YOLOv10. It compares these architectures through their design choices, feature-fusion strategies, loss functions, deployment trade-offs, and reported benchmark performance. The paper also summarizes commonly used autonomous-driving datasets, evaluation metrics, open challenges, and future research directions. Overall, this survey highlights how one-stage detectors balance speed, accuracy, efficiency, and robustness, while also emphasizing the remaining gap between benchmark results and dependable real-world autonomous-driving performance.
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