RBNet: An Ultrafast Rendering-Based Architecture for Railway Defect Segmentation

Abstract

Inspection of railway defects is crucial for the safe and efficient operation of trains. Recent advancements in convolutional neural networks have led to the development of many effective detection and segmentation algorithms, however, these algorithms often struggle to balance efficiency and precision. In this paper, we present a rendering-based fully convolutional network that generates segmentation results through a coarse-to-fine approach. This allows our framework to make full use of low-level features while minimizing the number of parameters. Additionally, our network generates segmentation results from multiple scales of the feature map and uses residual connections to improve low-level feature detection. To improve training, we propose a novel method that augments the dataset by cutting and pasting images and corresponding ground truth labels horizontally. To better understand the patterns learned by our model, we also generate importance and uncertainty maps to make our model explainable. Our results show that the proposed method outperforms other state-of-the-art image segmentation methods with a higher frame rate and better performance.