Summary of A Resource Efficient Fusion Network For Object Detection in Bird’s-eye View Using Camera and Raw Radar Data, by Kavin Chandrasekaran et al.
A Resource Efficient Fusion Network for Object Detection in Bird’s-Eye View using Camera and Raw Radar Data
by Kavin Chandrasekaran, Sorin Grigorescu, Gijs Dubbelman, Pavol Jancura
First submitted to arxiv on: 20 Nov 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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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 A novel approach to fusion-based object detection is proposed, combining raw range-Doppler (RD) spectrum data from radar sensors with camera images. The authors bypass traditional radar signal processing and instead focus on directly utilizing the RD spectrum. Camera images are transformed into Bird’s-Eye View (BEV) Polar domain, extracting features using an encoder-decoder architecture. These feature maps are then fused with Range-Azimuth (RA) features recovered from the RD spectrum, leveraging a camera encoder-decoder model. The fusion strategy is evaluated on the RADIal dataset in terms of accuracy and computational complexity, outperforming existing methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Cameras and radar sensors can help vehicles “see” their surroundings. Radar sensors are good at working in bad weather, but they don’t give us as much information about what’s around us as cameras do. In this research, scientists found a new way to use both types of sensors together. They took the raw data from the radar sensor and used it right away, without processing it first. At the same time, they processed camera images in a special way to get important features out of them. Then, they combined these features with information from the radar sensor to detect objects like cars or pedestrians. The new method worked well on a test dataset and was faster than other methods. |
Keywords
» Artificial intelligence » Encoder decoder » Object detection » Signal processing
