Event-Based Image Semantic Segmentation

Semantic segmentation technology is crucial for image processing in practical scenarios such as autonomous driving. However, semantic segmentation based on traditional cameras still faces challenges such as information loss under dynamic lighting and motion blur of high-speed moving objects. Therefore, this paper introduces event cameras with high dynamic range and high response speed, which can effectively image under harsh lighting and high-speed motion conditions, and proposes an Event-based Image Semantic Segmentation (EVISS) method based on event cameras. In the dataset section, a large-scale and high-precision annotated dataset, Carla-Semantic, was created through CARLA simulation environment to address the issue of a limited number of event camera based semantic segmentation datasets and low annotation quality. In the network design section, to address the problem of difficult feature extraction from event data with uneven distribution, the EVISS method enhances the global connectivity and context dependency of images through an improved graph Laplace formula and attention mechanism, effectively extracting high-level event features. The experimental results on the Carla-Semantic dataset and the existing method Ev-SegNet show that our EVISS method achieves 87.89% and 81.93% in MPA and mIoU evaluation metrics, respectively, surpassing the performance of the comparative methods and effectively achieving image semantic segmentation based on event cameras.