Esnet: Edge-Based Segmentation Network for Real-Time Semantic Segmentation in Traffic Scenes

Semantic segmentation is widely used in the industry recently, especially in the field of scene understanding, surveillance and autonomous driving. However, majority of current state-of-the-art algorithms run accompany with high consumption of computation resources. Thus, our work focuses on real-time semantic segmentation which could reduce a large proportion of computation. Traditional methods to speed up segmentation process tend to down sample image. However, down sampling would cause the loss of information. Hence, we propose a real-time edge-based segmentation network (ESNet) that incorporate high-resolution global edge information with low-resolution classification-level semantic information. Our network performs real-time inference on single GPU card on high-resolution Cityscapes dataset.