FSN-Swin: A Network for Freespace Detection in Unstructured Environments

In this paper, we propose an unstructured road-free space detection method that integrates distance imaging information in the Transformer framework. The proposed network is FNS-Swin, which innovatively supplements features through data fusion to reduce the dependence of Transformer networks applied to computer vision on RGB image data volume while improving the accuracy of segmentation algorithms. Regarding the network framework, we adopted Swin Transformer’s sliding window structure. We reset the number of layers of the network model to better adapt to learning fused features. It dynamically fuses distance and image geometric features by adding a cross-attention mechanism, enhancing the data correlation between the two representations. In the depth information extraction section, we used the SNE model to extract the depth features of depth images and convert them into surface normal vector maps, enhancing the correlation between depth information and road plane prediction. To verify the effectiveness of the proposed algorithm, we conducted testing experiments on unstructured ORFD datasets and KITTI-Road datasets, respectively. The experimental results show that our algorithm outperforms others in multiple metrics, achieving the best overall performance.