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cloud.google.com·7h ago
AI Research
arXiv CS.AI·Aug 20·5 min read
One-Stage Object Detectors in Autonomous Driving
TL;DR
One-stage object detectors, such as YOLOv10 and EfficientDet, are crucial for real-time perception in autonomous driving. This survey analyzes their evolution, performance, and challenges in achieving reliable detection.
✦ Why It Matters
Evaluate your current object detection models against the latest one-stage detectors to enhance performance.
Key Takeaways
- Explore the latest one-stage detectors for improved detection speed.
- Assess the trade-offs between accuracy and efficiency in your models.
- Utilize benchmark datasets to evaluate your detection systems.
- Identify open challenges in autonomous driving for future research.
Full Summary
Autonomous vehicles require efficient perception systems to identify various road objects like vehicles and pedestrians in real time. This survey focuses on one-stage object detectors, which process images in a single pass, making them faster than two-stage counterparts.
Key models reviewed include YOLOv1, SSD, RetinaNet, and the latest YOLOv10, highlighting their design choices, feature-fusion strategies, and loss functions. The paper compares these models based on benchmark performance and deployment trade-offs, revealing a persistent gap between ideal benchmark results and real-world application reliability.
Additionally, it discusses commonly used datasets and evaluation metrics, while outlining open challenges and future research directions. The findings emphasize the need for ongoing improvements in speed, accuracy, and robustness for practical autonomous driving applications.