Spike-BRGNet: Efficient and Accurate Event-Based Semantic Segmentation With Boundary Region-Guided Spiking Neural Networks

Event-based semantic segmentation in traffic scenes has attracted considerable attention in autonomous driving systems due to the advantages of event cameras such as high dynamic range, low latency, and low energy consumption. However, existing Artificial Neural Network (ANN)-based methods rely on conventional image frames, often neglecting the spatial-temporal dynamics inherent in event streams and consuming higher energy costs, significantly limiting their applicability in energy-constrained environments. In this study, we introduce Spike-BRGNet, a Spike-driven Boundary Region-Guided Network that efficiently extracts boundary information utilizing only events to guide the segmentation encoder, while preserving the energy efficiency of Spiking Neural Networks (SNNs). Specifically, to explore the implicit information from events, we design a three-branch spiking encoder that consists of semantic detail (SD), context aggregation (CA), and boundary aware (BA) branches to capture specific features. Then, a spiking multi-scale context aggregation (SMSCA) module is proposed to enhance the semantics of the CA branch. Finally, a novel boundary region-guided loss function and a dynamic surrogate gradient function, EvAF, are designed to optimize the model. Extensive experiments show that our model outperforms state-of-the-art (SOTA) SNN-based methods on DDD17 (+1.57%) and DSEC dataset (+1.91%). Furthermore, Spike-BRGNet consumes <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$17.76\times $ </tex-math></inline-formula> less energy than ANN-based models, showing superior energy-saving performance.