Abstract: Traditional track segmentation methods cannot meet the real-time and accurate requirements for perception of track areas during train operation. This paper studied a track region segmentation method based on RGBD fusion images and improved U-net. The RGB images were fused with depth images to obtain RGBD fusion images, which were input into the improved U-net to establish a track region segmentation model. After experimental verification, compared with the U-net model that only inputs RGB images, the F1 value of the track region segmentation model has been improved by about 0.28, the Mean Intersection over Union (MIoU) has been improved by about 0.1, and the Pixel Accuracy (PA) has been improved by 0.0026, proving its higher accuracy in track region segmentation. At the same time, it has been verified that the network performance of the model has also been significantly improved.