Enhanced Neuromorphic Semantic Segmentation Latency through Stream Event
This work addresses latency and energy efficiency challenges for real-time applications in dynamic environments, but it appears incremental as it builds on existing event-based and SNN methods.
The paper tackles the problem of high latency in semantic segmentation for real-time systems like UAVs and self-driving cars by using event streams from event-based cameras and a Spiking Neural Network, resulting in significantly reduced latency with only a limited drop in accuracy on the DSEC dataset.
Achieving optimal semantic segmentation with frame-based vision sensors poses significant challenges for real-time systems like UAVs and self-driving cars, which require rapid and precise processing. Traditional frame-based methods often struggle to balance latency, accuracy, and energy efficiency. To address these challenges, we leverage event streams from event-based cameras-bio-inspired sensors that trigger events in response to changes in the scene. Specifically, we analyze the number of events triggered between successive frames, with a high number indicating significant changes and a low number indicating minimal changes. We exploit this event information to solve the semantic segmentation task by employing a Spiking Neural Network (SNN), a bio-inspired computing paradigm known for its low energy consumption. Our experiments on the DSEC dataset show that our approach significantly reduces latency with only a limited drop in accuracy. Additionally, by using SNNs, we achieve low power consumption, making our method suitable for energy-constrained real-time applications. To the best of our knowledge, our approach is the first to effectively balance reduced latency, minimal accuracy loss, and energy efficiency using events stream to enhance semantic segmentation in dynamic and resource-limited environments.