The geometry status of electric railway catenary is critical to the stability of railway power supplying. An accurate and efficient vision measurement approach for railway catenary geometry parameters (CGPs) is proposed. An image-based system using a binocular vision component composed of two line scan cameras is first described and analyzed, and then a calibration method for the vision component based on 1-D target combining with 1-D displacement platform is investigated. When the onsite inspection is performed by the test train, there are multiple degree of freedom vibrations created by onsite irregularities of railway line. The detection accuracy of CGPs is inevitably degraded due to the vibrations on the results during running. A compensation method to reduce the vibration errors is presented. Experiments carried out in real-life detections validate the effectiveness and efficiency of the proposed system.

As a key component of the high-speed railway catenary, the insulator plays an important role in supporting the catenary and maintaining the insulation between the catenary and earth. Regular inspection using computer vision technology is an effective way to detect insulator defects and improve the catenary operation safety. However, achieving full automation of insulator defects detection is still a difficult task due to the defective sample scarcity and subtle defect features. To overcome the problem, this paper proposes a defect detection GANs (DefGAN) that consists of a denoising auto-encoder, a discriminator and a classifier to detect defects, which can generate defective samples from pitted latent representations, thereby improving the reliability of defect detection classifier. The proposed DefGAN can pit the latent representations of normal samples to generate defective samples. Our proposal is mainly consists of two stages. In the first stage, we use a cascaded deep segmentation networks (CDSNets) to extract the insulator from the catenary images. Then the insulator area is sampled into small patches. In the second stage, the patches are fed into the proposed DefGAN to detect defects. Moreover, the defect score are determined by the anomaly probability of the classifier and reconstruction error of the denoising auto-encoder. The effectiveness of our work is measured across the catenary datasets.

Railway tunnel 3D clearance inspection is critical to guaranteeing railway operation safety. However, it is a challenge to inspect railway tunnel 3D clearance using a vision system, because both the spatial range and field of view (FOV) of such measurements are quite large. This paper summarizes our work on dynamic railway tunnel 3D clearance inspection based on a multi-camera and structured-light vision system (MSVS). First, the configuration of the MSVS is described. Then, the global calibration for the MSVS is discussed in detail. The onboard vision system is mounted on a dedicated vehicle and is expected to suffer from multiple degrees of freedom vibrations brought about by the running vehicle. Any small vibration can result in substantial measurement errors. In order to overcome this problem, a vehicle motion deviation rectifying method is investigated. Experiments using the vision inspection system are conducted with satisfactory online measurement results.

The core task of intelligent detection in electrified railway catenaries is to detect their supporting components. However, due to the large number and variety of components in catenary inspection images, labeled catenary data is often limited, and few studies have focused on leveraging large amounts of unlabeled datasets in this field. This article proposes a novel self-supervised pretraining model catenary support rod masking-based masked image modeling (CSRM-MIM) for detecting catenary support components (CSCs), which effectively utilizes the valuable information in unlabeled catenary data. Specifically, a new semantic catenary support rod area masking (CSRA masking) strategy based on the CSRA features is proposed to guide the model in learning meaningful catenary semantic information during the pretraining process. Additionally, a new Siamese pretrained network framework is designed, incorporating an information interaction enhancement module (IIEM) and a dual reconstruction network to perform dual reconstruction on the semantic mask, thereby extracting global features in the catenary domain. Finally, a multiscale knowledge distillation (MSKD) strategy optimized by the cross-layer fusion module cross-layer fusion decoding (CLFD) is introduced to assist the self-supervised model in acquiring specific-general representations for the catenary field and transferring multiscale information, which benefits subsequent detection tasks. Experimental results demonstrate that this method significantly enhances the detector’s performance in identifying CSCs, owing to the retained self-supervised learning (SSL) strategy.

Automatic inspections of overhead contact lines (OCLs) are developed to implement anomaly detection during normal operation. It is an essential prerequisite for efficient maintenance of railway electrification system. This paper presents a comprehensive survey on the inspections of OCLs with an emphasis on computer vision technology, which has developed rapidly due to its ability to understand images. Our survey begins with a brief introduction on anomalies in OCL inspections and generic procedures of computer vision inspection for anomaly detection. Subsequently, for detecting deviations of parameters and defective components during OCL inspections, the existing techniques involving stereo vision and object vision, especially convolution neural networks are described in detail from two aspects: measurement of OCL parameters and identification of OCL conditions. Some interference factors in OCL inspection are analyzed. Actual cases of the inspections are also briefly shown. Challenges and suggestions for further research on OCL inspection are drawn toward the end of the paper.

The swivel clevis (SC) is a vulnerable part of the Overhead Catenary System (OCS). Regular inspection using computer vision technology is an effective way to detect SC defects and improve the OCS operation safety. However, achieving full automation of SC defects detection is still a difficult task due to defective sample scarcity and data distribution shift. To overcome these problems, this paper proposes a novel defects detection method that combines an adaptive SC segmentation network (Adaptive SSN) and local operators. During the inspection process, an unreliability index defined by the model uncertainty and prior knowledge is used to monitor the reliability of the Adaptive SSN. When data distribution shift causes the Adaptive SSN to be unreliable, human annotator will be asked to update the training set and retrain the Adaptive SSN to adapt to the new data distribution. Then the geometric features obtained from segmentation masks and the local features extracted by local operators are used to detect the SC defects. Effectiveness of the proposed method is demonstrated by the experimental results on the data from several high-speed railway lines.