A stable track components segmentation model should have consistent performance across a broad spectrum of railroad conditions, particularly in unfamiliar locations. Despite this, satisfying this requirement proves challenging when working with a limited track dataset, as there is a substantial domain shift between the given dataset and unobserved distributions. The goal of this paper is to improve the generalization ability of track component segmentation in situations where single-domain training data is available. Toward this end, a novel track component instance segmentation method combining the geometric context is proposed. First, we design an initial mask prediction head (IMPH) that utilizes predicted box output from the object detector to generate initial masks by merging the geometric priors of track components. Meanwhile, a multiscale feature fusion structure is introduced to encourage IMPH to better capture the geometric context. Then, a final mask refinement head (FMRH) is introduced to get higher quality masks with a geometry-gated aggregation strategy. On the basis of experiments on track datasets, it was determined that the heads can be incorporated with several types of detection frameworks and have demonstrated consistent generalization enhancements across multiple object detectors. Furthermore, our method substantially enhances segmentation performance on various unobserved domains.

The fastener is one of the main components of a rail track system. In recent years, deep learning methods such as image segmentation have greatly boosted the fastener state detection process. However, there is still a need to improve the segmentation accuracy and speed, especially for the fasteners in complex environments. To handle this problem, a fast and accurate fastener semantic segmentation network named RFS-Net is proposed based on shape guidance, which can offer a better speed/accuracy trade-off performance via a very shallow architecture. Specifically, in the encoder, a two-stream structure (i.e., regular stream and shape stream) that processes the fastener and shape image in parallel is introduced. The shape image is created based on the geometric structure of the fastener, and it is served as input to the shape stream to guide the segmentation of the fastener. The decoder integrates deep features from the two-stream encoder and then recovers the shape information by the shape attention blocks with skipping connections. We provide two versions of RFS-Net: RFS-Net_S (1.0M, 1014FPS) and RFS-Net_L (12.01M, 453FPS) on the NVIDIA RTX 3060. Experimental results demonstrate the effectiveness of our method by achieving a promising trade-off between accuracy and inference speed. In particular, our method is faster and more accurate on a challenging dataset, from fast modes: 1014 FPS for RFS-Net_S versus 724 FPS for Segmenter, to high-quality segmentation: better performance than STDC with nearly one percent (92.36% versus 91.48% Mean IoU score).

It has long been recognized that the standard convolution is not rotation equivariant and thus not appropriate for downside fisheye images which are rotationally symmetric. This paper introduces Rotational Convolution, a novel convolution that rotates the convolution kernel by characteristics of downside fisheye images. With the four rotation states of the convolution kernel, Rotational Convolution can be implemented on discrete signals. Rotational Convolution improves the performance of different networks in semantic segmentation and object detection markedly, harming the inference speed slightly. Finally, we demonstrate our methods' numerical accuracy, computational efficiency, and effectiveness on the public segmentation dataset THEODORE and our self-built detection dataset SEU-fisheye. Our code is available at: https://github.com/wx19941204/Rotational-Convolution-for-downside-fisheye-images.

Defective fastener images detection is an essential task in the vision-based railway track safety inspection. Although existing methods have achieved some level of success, the detection accuracy in this field suffers from the defective fasteners being far less common than normal fasteners. One way to tackle this problem is to expand the defect sample. However, current state-of-the-art defective fastener generation methods mainly rely on generative adversarial networks or simply augment the defect data through traditional image processing. These methods may not be ideal as it is difficult to produce images with high quality and rich diversity at the same time. This paper proposes a new method for fastener sample generation that actively divides the sample generation into two independent parts: defective foregrounds generation and complete backgrounds generation. The key to this method is to generate foregrounds and backgrounds based on geometric constraint and image inpainting, respectively. Specifically, we adopt a skeleton mapping algorithm to directionally control the generated types of defective foregrounds. Meanwhile, an image inpainting network is employed to expand the background. The experiments show that this enables us to generate better-quality and richer-diversity images by combining deep learning and image processing advantages. To the best of our knowledge, our method is the first to achieve state-of-the-art performance, i.e., the classification accuracy reaches 97.97%, without using real defective fastener images during the defect classification network training process.