Densely Multiscale Fusion Network for Lightweight and Accurate Semantic Segmentation of Railway Scenes

Semantic segmentation in railway scenes constitutes a fundamental task in the context of autonomous train driving, serving as a critical source of positional information for other visual tasks. Yet, prior semantic segmentation algorithms have faced challenges in achieving real-time and precise railway panoptic segmentation. Railway scene images often have highly imbalanced content density. Hence, the distribution of different regions in the image is highly nonuniform, which leads to the issue of semantic sample imbalance. To achieve this, we propose a dense multiscale fusion network (DMFNet), a lightweight and precise semantic segmentation framework tailored for railway scenes. DMFNet employs a network framework that combines dense connections and multiscale fusion to efficiently extract and utilize features. To enhance the feature extraction capability of the neural network, we design a lightweight residual block with multiple scales, composed of large convolution kernels. Additionally, we introduce a novel method for reassigning loss function weights to balance the samples within railway scenes. Extensive experimentation demonstrates that our DMFNet achieves outstanding results on railway scene datasets. It is designed to provide a lightweight and accurate semantic segmentation solution for railway scenes.