RT-GAN: GAN Based Architecture for Precise Segmentation of Railway Tracks

Abstract

Identifying and locating track areas in images through machine vision technology is the primary task of autonomous UAV inspection. Aiming at the problems that railway track images are greatly affected by light and perspective, the background environment is complex and easy to misidentify, and existing methods are difficult to reason correctly about the obscured track area, this paper proposes a generative adversarial network (GAN)-based railway track precision segmentation framework, RT-GAN. RT-GAN consists of an encoder–decoder generator (named RT-seg) and a patch-based track discriminator. For the generator design, a linear span unit (LSU) and linear extension pyramid (LSP) are used to concatenate network features with different resolutions. In addition, a loss function containing gradient information is designed, and the gradient image of the segmentation result is added into the input of the track discriminator, aiming to guide the generator, RT-seg, to focus on the linear features of the railway tracks faster and more accurately. Experiments on the railway track dataset proposed in this paper show that with the improved loss function and adversarial training, RT-GAN provides a more accurate segmentation of rail tracks than the state-of-the-art techniques and has stronger occlusion inference capabilities, achieving 88.07% and 81.34% IoU in unaugmented and augmented datasets.