Research on the Segmentation and Extraction of Scenes Along Railway Lines Based on Remote Sensing Images of UAVs

At present, the manual inspection along railway lines is still a major method to ensure railway operation safely, but the cost is high and work efficiency is low. Therefore, unmanned aerial vehicles (UAVs) patrol inspection is required. This paper presents the effective segmentation of scenes along railway lines (SRL) from remote sensing perspective of UAVs based on the full convolutional networks (FCN). Firstly, the datasets needed in this research are collected and produced from Langfang section of the Beijing–Shanghai high-speed railway. The datasets are expanded by using data augmentation to constrain the overfitting in the training process. Secondly, the segmentation model FCN-8s for SRL is developed and trained. The related setting and hardware environment in the training process are described in this paper. The experimental results show that a single image prediction needs 151.2 ms, to achieve 6.6 fps when input size is 384 × 384. Good accuracy is obtained on the test dataset, i.e., 55.8% MIoU and 70.2% MPA, which meets the expectations of FCN. At the same time, it is also found that the segmentation of railway area achieves the best result thus the railway area is extracted accordingly.