SpikingViT: a Multi-scale Spiking Vision Transformer Model for Event-based Object Detection

Event cameras have unique advantages in object detection, capturing asynchronous events without continuous frames. They excel in dynamic range, low latency, and high-speed motion scenarios, with lower power consumption. However, aggregating event data into image frames leads to information loss and reduced detection performance. Applying traditional neural networks to event camera outputs is challenging due to event data’s distinct characteristics. In this study, we present a novel spiking neural networks-based (SNNs) object detection model, the Spiking Vision Transformer (SpikingViT) to address these issues. Firstly, we design a dedicated event data converting module that effectively captures the unique characteristics of event data, mitigating the risk of information loss while preserving its spatio-temporal features. Secondly, we introduce SpikingViT, a novel object detection model that leverages SNNs capable of extracting spatio-temporal information among events data. SpikingViT combines the advantages of SNNs and Transformer models, incorporating mechanisms such as attention and residual voltage memory to further enhance detection performance. Extensive experiments have substantiated the remarkable proficiency of SpikingViT in event-based object detection, positioning it as a formidable contender. Our proposed approach adeptly retains spatio-temporal information inherent in event data, leading to a substantial enhancement in detection performance.