Efficient Dual-Stream Fusion Network for Real-Time Railway Scene Understanding

Railway scene understanding is key to autonomous train operation and important in active train perception. However, most railway scene understanding methods focus on track extraction and ignore other components of railway scenes. Although several semantic segmentation algorithms are used to identify railway scenes, they are computationally expensive and slow with limits applications in railways. To solve these problems, we propose efficient dual-stream fusion network (EDFNet), a lightweight semantic segmentation algorithm, for understanding railway scenes. First, a dual-stream backbone network based on mobile inverted residual blocks is proposed to extract and fuse detailed features and semantic features. Next, a bi-directional feature pyramid pooling module is proposed to obtain multi-scale features and deep semantic features. Finally, a multi-task aggregate loss is designed to learn semantic and boundary information, thus improving the accuracy without increasing the computational complexity. Extensive experimental results demonstrate that EDFNet outperforms the lightweight state-of-the-art algorithms with high accuracy and fast speed on two railway datasets.