Spiking Neural Networks for Fast-Moving Object Detection on Neuromorphic
  Hardware Devices Using an Event-Based Camera
Andreas Ziegler Karl Vetter Thomas Gossard Jonas Tebbe Andreas Zell
Table tennis is a fast-paced and exhilarating sport that demands agility,
precision, and fast reflexes. In recent years, robotic table tennis has become
a popular research challenge for robot perception algorithms. Fast and accurate
ball detection is crucial for enabling a robotic arm to rally the ball back
successfully. Previous approaches have employed conventional frame-based
cameras with Convolutional Neural Networks (CNNs) or traditional computer
vision methods. In this paper, we propose a novel solution that combines an
event-based camera with Spiking Neural Networks (SNNs) for ball detection. We
use multiple state-of-the-art SNN frameworks and develop a SNN architecture for
each of them, complying with their corresponding constraints. Additionally, we
implement the SNN solution across multiple neuromorphic edge devices,
conducting comparisons of their accuracies and run-times. This furnishes
robotics researchers with a benchmark illustrating the capabilities achievable
with each SNN framework and a corresponding neuromorphic edge device. Next to
this comparison of SNN solutions for robots, we also show that an SNN on a
neuromorphic edge device is able to run in real-time in a closed loop robotic
system, a table tennis robot in our use case.