发表机构
Florida Gulf Coast University（佛罗里达海湾海岸大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文针对自动驾驶场景调研分析单阶段目标检测器，涵盖其演进、架构对比、相关数据集与指标等，指出该类检测器需平衡速度等性能且与实际应用仍有差距。
AI 中文摘要
自动驾驶车辆依赖快速且可靠的感知系统，实时检测周围的车辆、行人、骑行者、交通标志及其他道路物体。本文针对自动驾驶场景对单阶段目标检测器开展全面调研与分析，而非提出新的检测系统实现。调研涵盖主要单阶段检测器的演进，包括YOLOv1、SSD、RetinaNet、EfficientDet，以及FCOS、CenterNet等无锚框检测器，还有YOLOv10等最新实时模型。本文从设计选择、特征融合策略、损失函数、部署权衡及报告的基准性能等方面对这些架构进行对比，还总结了常用的自动驾驶数据集、评估指标、公开挑战及未来研究方向。总体而言，该调研阐明了单阶段检测器如何在速度、精度、效率与鲁棒性间取得平衡，同时强调了基准结果与可靠的实际自动驾驶性能之间仍存在差距。
英文摘要
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.
