RailSAM: Taming SAM with Adapter for Railway Segmentation

The emergence of vision foundation model, such as the Segment Anything Model (SAM), has brought groundbreaking advancements to downstream tasks due to its zero-shot transfer capability. In this work, RailSAM is proposed by incorporating the SAM generalist architecture with domain-specific adapter for railway segmentation. By leveraging the pretrained knowledge from the frozen SAM encoder, we seamlessly inject domain-specific information through visual prompts into the mask decoder via carefully designed adapters. On the publicly available RailSem19 dataset, our experiments demonstrate that RailSAM significantly outperforms existing task-specific methods and exhibits remarkable robustness under challenging conditions. We hope this work would inspire in-depth exploration of vision foundation model for intelligent railway transportation. The code will be open-source soon.