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

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 article, 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. In addition, 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 the images and the corresponding ground-truth labels horizontally. Our results show that the proposed method outperforms other state-of-the-art image segmentation methods with a higher frame rate and better performance.