EPNet: An Efficient Postprocessing Network for Enhancing Semantic Segmentation in Autonomous Driving

Semantic segmentation is of great importance in the field of autonomous driving, as it provides semantic information for a scene that intelligent vehicles need to interact with. Although a large number of different semantic segmentation networks have been proposed, achieving high performance for semantic segmentation in real-time using a lightweight network is challenging in practical conditions. In this article, we propose an efficient postprocessing network that can be applied to various real-time semantic segmentation networks to enhance their performance. Specifically, we introduce a transformer-based lightweight network to obtain information for refining the output of a given semantic segmentation network. Our network has very limited parameters and can work in real-time and a plug-and-play manner to enhance the performance of different semantic segmentation networks. This capability can significantly benefit real-time perception in autonomous driving applications. We demonstrate the effectiveness of our network through extensive experiments showing that it can improve the performance of various semantic segmentation networks.