Spiking Neural Networks for Fast-Moving Object Detection on Neuromorphic Hardware Devices Using an Event-Based Camera

• URL: http://arxiv.org/abs/2403.10677v1
• Date: Fri, 15 Mar 2024 20:53:10 GMT
• Title: Spiking Neural Networks for Fast-Moving Object Detection on Neuromorphic Hardware Devices Using an Event-Based Camera
• Authors: Andreas Ziegler, Karl Vetter, Thomas Gossard, Jonas Tebbe, Andreas Zell,
• Abstract summary: We propose a novel solution that combines an event-based camera with Spiking Neural Networks (SNNs) for ball detection. We implement the SNN solution across multiple neuromorphic edge devices, conducting comparisons of their accuracies and run-times. 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.
• Score: 11.735290341808064
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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