Summary of Sfod: Spiking Fusion Object Detector, by Yimeng Fan et al.
SFOD: Spiking Fusion Object Detector
by Yimeng Fan, Wei Zhang, Changsong Liu, Mingyang Li, Wenrui Lu
First submitted to arxiv on: 22 Mar 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 object detection in event cameras using Spiking Neural Networks (SNNs) is proposed, addressing the challenges posed by sparse and asynchronous event data. The Spiking Fusion Object Detector (SFOD) combines feature maps from different scales within SNNs, achieving state-of-the-art classification results on the NCAR dataset with 93.7% accuracy. This outperforms existing SNN-based approaches. Furthermore, the SFOD achieves a state-of-the-art mean Average Precision (mAP) of 32.1% on the GEN1 detection dataset. The paper highlights the potential of SNNs in object detection and contributes to the advancement of this field. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Event cameras offer unique capabilities for object detection due to their high temporal resolution, dynamic range, low power consumption, and high pixel bandwidth. However, sparse and asynchronous event data pose challenges to existing object detection algorithms. Spiking Neural Networks (SNNs) inspired by brain function can help solve these difficulties. The paper proposes the Spiking Fusion Object Detector (SFOD), a simple and efficient approach for SNN-based object detection using event cameras. |
Keywords
» Artificial intelligence » Classification » Mean average precision » Object detection
