Summary of Dsfec: Efficient and Deployable Deep Radar Object Detection, by Gayathri Dandugula et al.
DSFEC: Efficient and Deployable Deep Radar Object Detection
by Gayathri Dandugula, Santhosh Boddana, Sudesh Mirashi
First submitted to arxiv on: 10 Dec 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper explores efficient radar object detection models for resource-constrained edge devices like the Raspberry Pi. Depthwise Separable Convolutions are integrated into a radar object detection network, along with a novel Feature Enhancement and Compression (FEC) module. The proposed DSFEC-L model outperforms the baseline on the nuScenes dataset, while also reducing computational power and memory usage. Two versions of the model are presented: DSFEC-M, which improves performance by 14.6% and reduces GFLOPs by 60%, and DSFEC-S, which achieves a remarkable 78.5% reduction in GFLOPs with only a 3.76% performance improvement. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Radar object detection models are too big to run on small devices like the Raspberry Pi. This paper makes them smaller so they can be used on these devices. They use special kinds of computer vision techniques and a new way to make features more important. The new model works better than before and uses less power, making it perfect for edge devices. |
Keywords
» Artificial intelligence » Object detection
