Survey paper analyzes one-stage object detectors for autonomous driving

A new survey paper provides a comprehensive analysis of one-stage object detectors crucial for autonomous driving systems. It traces the evolution of these detectors, from early models like YOLOv1 and SSD to more recent architectures such as EfficientDet and YOLOv10. The paper compares their design choices, feature fusion, loss functions, and performance benchmarks, while also discussing datasets, evaluation metrics, and ongoing challenges in achieving dependable real-world performance. AI

IMPACT Provides a consolidated overview of object detection techniques, aiding researchers and developers in autonomous driving.

RANK_REASON The cluster contains a survey paper analyzing existing research in a specific AI subfield. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

• arXiv
• centerNet
• EfficientDet: Scalable and Efficient Object Detection
• FCOS
• RetinaNet
• YOLOv1
• YOLOv10

• paper
• other

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