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About this paper
Yuhan Zhou, Mehri Sattari, Haihua Chen, Kewei Sha
Researchers at the University of North Texas developed a systematic framework to model and quantify redundancy in multisource camera and multimodal image-LiDAR datasets used for autonomous driving. Their analysis demonstrated that selectively reducing redundant data can enhance or maintain object detection performance while improving data efficiency.
Key insights
- Redundancy in autonomous driving datasets is a quantifiable and actionable data quality factor that significantly impacts perception system performance and efficiency.
- Pruning redundant multisource camera data based on object completeness (BCS) can improve or maintain object detection accuracy while reducing dataset size.
- Cross-modal redundancy between camera and LiDAR detections is notably higher for objects closer to the ego-vehicle, indicating opportunities for distance-aware data pruning.
Summary by alphaXiv.
Tip: change arxiv.org to autoarxiv.org in any arXiv abstract link to open the paper here.