One-Stage Object Detectors in Autonomous Driving
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 c
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Paper → model → repo connections mined from source citations (Tier-1 exact match).
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- FuzzyOverlapping authors or contributors · 62%open-webui/open-webui →
“Shared author/contributor keys: nguyen”
- LinkedLinked via arxiv author · 85%Jonel Roman →
“One-Stage Object Detectors in Autonomous Driving”
- LinkedLinked via arxiv author · 85%Ryan Sirjue →
“One-Stage Object Detectors in Autonomous Driving”
- LinkedLinked via arxiv author · 85%Peter Nguyen →
“One-Stage Object Detectors in Autonomous Driving”
- LinkedLinked via arxiv author · 85%Daniel Krutky →
“One-Stage Object Detectors in Autonomous Driving”
- LinkedLinked via arxiv author · 85%Juan Jesus →
“One-Stage Object Detectors in Autonomous Driving”
- LinkedLinked via arxiv author · 85%Sudip Dhakal →
“One-Stage Object Detectors in Autonomous Driving”