Problem
Research questions and friction points this paper is trying to address.
one-stage object detectors
autonomous driving
real-time detection
benchmark performance
reliable perception
One-Stage Object Detectors in Autonomous Driving
🤖 AI Summary
本文综述并分析了一阶段目标检测器在自动驾驶中的应用，通过对比不同架构的设计选择、性能等，探讨了其在速度、准确性和效率等方面的平衡及挑战。
📝 Abstract
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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
one-stage detectors
autonomous driving
feature-fusion strategies
loss functions
benchmark performance
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