Leveraging Transformer Decoder for Automotive Radar Object Detection

arXiv:2601.13386 · cs.CV, eess.SP · Submitted 2026-01-19 · Read on arXiv

cs.CV, eess.SP

Submitted: 2026-01-19

Updated: 2026-01-19

Journal ref: 2026 IEEE Radar Conference (RadarConf26), IEEE, 2026

DOI: 10.1109/RadarConf2663773.2026.11675417

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Transformer — 43× in this paper · explained in 2 episodes
• Accuracy — 4× in this paper · explained in 1 episode
• Pyramid Token Fusion — 4× in this paper
• Attention Mechanism — 3× in this paper · explained in 2 episodes
• Alignment — 2× in this paper · explained in 1 episode
• Average Precision — 2× in this paper
• Benchmark — 2× in this paper · explained in 2 episodes
• Embeddings — 2× in this paper · explained in 2 episodes
• Feature Extraction — 2× in this paper · explained in 2 episodes
• Temporal Correlations — 2× in this paper · explained in 2 episodes
• AdamW Optimizer — 1× in this paper · explained in 1 episode
• D CNNs to Range-Azimuth-Doppler — 1× in this paper
• Data Augmentation — 1× in this paper · explained in 7 episodes
• Generalization — 1× in this paper · explained in 13 episodes
• Iterative Refinement — 1× in this paper · explained in 3 episodes
• Loss Function — 1× in this paper · explained in 2 episodes
• Robustness — 1× in this paper · explained in 5 episodes

Sources

• Deformable DETR: Deformable Transformers for End-to-End Object Detection
• YOLOv4: Optimal Speed and Accuracy of Object Detection

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