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Trainable Spiking-YOLO for low-latency and high-performance object detection.

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Neural Networks : the Official Journal of the International Neural Network Society
|January 11, 2024
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Summary

This study introduces Trainable Spiking-YOLO (Tr-Spiking-YOLO), an efficient spiking neural network for object detection. It achieves high accuracy and speed on edge devices, outperforming traditional models.

Keywords:
Dynamic vision sensorEdge computingObject detectionSpiking neural network

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Neuromorphic Engineering

Background:

  • Spiking neural networks (SNNs) offer efficient, event-driven computation suitable for edge AI.
  • Challenges remain in achieving high accuracy and speed for SNNs in object detection tasks.

Purpose of the Study:

  • To develop an end-to-end trainable Spiking-YOLO (Tr-Spiking-YOLO) model for low-latency, high-performance object detection.
  • To evaluate the model's effectiveness on both frame-based and event-based datasets.
  • To analyze the impact of different decoding methods on detection performance.

Main Methods:

  • Proposed an end-to-end trainable Spiking-YOLO (Tr-Spiking-YOLO) architecture.
  • Evaluated the model on PASCAL VOC (frame-based) and GEN1 Automotive Detection (event-based) datasets.
  • Investigated the influence of various decoding strategies on object detection outcomes.

Main Results:

  • Tr-Spiking-YOLO demonstrated competitive or superior performance in accuracy, latency, and energy consumption compared to ANN and conversion-based SNN models.
  • Achieved real-time detection (14-39 FPS) with desirable mean Average Precision (mAP) on edge devices.
  • Performance was influenced by the choice of decoding methods.

Conclusions:

  • Tr-Spiking-YOLO effectively addresses the challenges of SNNs in object detection, enabling efficient and accurate performance.
  • The model is suitable for real-time object detection on resource-constrained edge platforms.
  • Further research into decoding methods can optimize SNN-based object detection.