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Autonomous railway inspection with unmanned aerial vehicles (UAVs) has huge advantages over traditional inspection methods. As a prerequisite for UAV‐based autonomous following of railway lines, it is quite essential to develop intelligent railway track detection algorithms. However, there are no existing algorithms currently that can efficiently adapt to the demand for the various forms and changing inclination angles of railway tracks in the UAV aerial images. To address the challenge, this paper proposes a novel anchor‐adaptive railway track detection network (ARTNet), which constructs a dual‐branch architecture based on projection length discrimination to realize full‐angle railway track detection for the UAV aerial images taken from arbitrary viewing angles. Considering the potential capacity imbalance of the two branches that can be caused by the uneven distribution of railway tracks in the dataset, a balanced transpose co‐training strategy is proposed to train the two branches coordinately. Moreover, an extra customized transposed consistency loss is designed to guide the training of the network without increasing any computational complexity. A set of experiments have been conducted to verify the feasibility and superiority of the ARTNet. It is demonstrated that our approach can effectively realize full‐angle railway track detection and outperform other popular algorithms greatly in terms of both detection accuracy and reasoning efficiency. ARTNet can achieve a mean F1 of 76.12 and run at a speed of 50 more frames per second. [ABSTRACT FROM AUTHOR]

Abstract Traction energy is the main component of railway operation energy, and a timetable that predefines the running time of train operation can be used to determine the traction energy consumption. This study proposes a bi-level model that optimizes timetables to achieve the energy-saving control of railway systems. The upper level of the model ensures the relative stability of the timetable while maintaining railway safety constraints, which makes train operations more convenient for the railway sector as well as passengers; while the lower level of the model optimizes the arrival and departure time among intermediate stations to minimize the energy consumption of each train. Then, a unified iterative optimization algorithm combining particle swarm is developed to solve the model, and a timetable that ensures energy consumption optimizations is thus obtained. A case study using actual operation data from the Beijing-Shanghai high-speed railway is developed to illustrate the proposed method. Results show that the total energy consumption is reduced by more than 7.6%, and the average adjustment time for each distance interval is approximately 1 min, which maintains the stability of the original timetable. Highlights • A bi-level model is developed to optimize railway energy and passenger convenience. • Energy saving, timetable stability, and train tracking constraints are investigated. • Spare time of timetable are optimally distributed by an iterative-loop algorithm. • Train optimal energy-saving operation strategies are proposed and verified by a case. • The model is applied in a real case saving 7.6% energy consumption. [ABSTRACT FROM AUTHOR]

• The high-speed railway network is modeled as a three-layer network. • Network-specific characteristics are considered in performance evaluation instead of pure complex network analysis. • Railway stations are prioritized and compared within different layers of the network. • The current physical rail network in China is generally sufficient to satisfy the requirements of its transportation accessibility. • Management implications are provided to facilitate the rail network planning, transportation organization and operations. High-speed railways, as dominant transportation patterns, constitute a large-scale complex network. Network performance is critical for high-level transportation services in transportation management. However, existing studies generally adopt pure topological indicators to evaluate performance in a single network and without considering the specific characteristics of rail transportation. This paper modeled the high-speed railway as a three-layer network including topological, functional, and service layers and assessed the integrated network performance from the view of transportation accessibility. In addition, the rail stations were prioritized and their performances in different layers were compared to analyze whether the total integrated performance was restricted by its connection abilities, functional capacities, and transportation service level. Finally, a case study of the high-speed rail network in China and comparisons with other traditional evaluation methods were conducted to verify the feasibility and superiority of the model. The findings of this study enable to provide management implications for rail transportation planning, organization, and operations. [ABSTRACT FROM AUTHOR]

Failure of the train axle box bearing will cause great loss. Now, condition-based maintenance of train axle box bearing has been a research hotspot around the world. Vibration signals generated by train axle box bearing have nonlinear and nonstationary characteristics. The methods used in traditional bearing fault diagnosis do not work well with the train axle box. To solve this problem, an effective method of axle box bearing fault diagnosis based on multifeature parameters is presented in this paper. This method can be divided into three parts, namely, weak fault signal extraction, feature extraction, and fault recognition. In the first part, a db4 wavelet is employed for denoising the original signals from the vibration sensors. In the second part, five time-domain parameters, five IMF energy-torque features, and two amplitude-ratio features are extracted. The latter seven frequency domain features are calculated based on the empirical mode decomposition and envelope spectrum analysis. In the third part, a fault classifier based on BP neural network is designed for automatic fault pattern recognition. A series of tests are carried out to verify the proposed method, which show that the accuracy is above 90%. [ABSTRACT FROM AUTHOR]

The state monitoring of the railway track line is one of the important tasks to ensure the safety of the railway transportation system. While the defect recognition result, that is, the inspection report, is the main basis for the maintenance decision. Most previous attempts have proposed intelligent detection methods to achieve rapid and accurate inspection of the safety state of the railway track line. However, there are few investigations on the automatic generation of inspection reports. Fortunately, inspired by the recent advances and successes in dense captioning, such technologies can be investigated and used to generate textual information on the type, position, status, and interrelationship of the key components from the field images. To this end, based on the work of DenseCap, a railway track line image captioning model (RTLCap for short) is proposed, which replaces VGG16 with ResNet-50-FPN as the backbone of the model to extract more powerful image features. In addition, towards the problems of object occlusion and category imbalance in the field images, Soft-NMS and Focal Loss are applied in RTLCap to promote defect description performance. After that, to improve the image processing speed of RTLCap and reduce the complexity of the model, a reconstructed RTLCap model named Faster RTLCap is presented with the help of YOLOv3. In the encoder part, a multi-level regional feature localization, mapping, and fusion module (MFLMF) are proposed to extract regional features, and an SPP (Spatial Pyramid Pooling) layer is employed after MFLMF to reduce model parameters. As for the decoder part, a stacked LSTM is adopted as the language model for better language representation learning. Both quantitative and qualitative experimental results demonstrate the effectiveness of the proposed methods. [ABSTRACT FROM AUTHOR]

Network efficiency analysis becomes important in railways in order to contribute towards improving the safety and capacity of the rail network, making rail travel more attractive for passengers, and improving industry practice and informing policy development. However, a physical railway network structure is a complicated system, and the operation, maintenance, and management of such a network is a difficult task which may be affected by many influential factors. By using efficiency analysis technology for a railway network, combining physical structure with operation functions can help railway industry to optimize the railway network while improving its efficiency and reliability. This paper presents a new methodology based on complex network principles that combines the physical railway structure with railway operation strategy for a railway network efficiency analysis. In this method, two network models of railway physical and train flow networks are developed for the identification of key stations in the railway network based on network efficiency contribution in which the terms of degree, strength, betweenness, clustering coefficient, and a comprehensive factor are taken into consideration. Once the key stations have been identified and analysed, the railway network efficiency is then studied on the basis of selective and random modes of the station failures. A case study is presented in this paper to demonstrate the application of the proposed methodology. The results show that the identified key stations in the railway network play an important role in improving the overall railway network efficiency, which can provide useful information to railway designers, engineers, operators and maintainers to operate and maintain railway network effectively and efficiently. [ABSTRACT FROM AUTHOR]

Aimed at the complicated problems of attraction characteristics regarding passenger flow in urban rail transit network, the concept of the gravity field of passenger flow is proposed in this paper. We establish the computation methods of field strength and potential energy to reveal the potential attraction relationship among stations from the perspective of the collection and distribution of passenger flow and the topology of network. As for the computation methods of field strength, an optimum path concept is proposed to define betweenness centrality parameter. Regarding the computation of potential energy, Compound Simpson’s Rule Formula is applied to get a solution to the function. Taking No. 10 Beijing Subway as a practical example, an analysis of simulation and verification is conducted, and the results shows in the following ways. Firstly, the bigger field strength value between two stations is, the stronger passenger flow attraction is, and the greater probability of the formation of the largest passenger flow of section is. Secondly, there is the greatest passenger flow volume and circulation capacity between two zones of high potential energy. [ABSTRACT FROM AUTHOR]

Split pins (SPs), as essential fasteners in the pantograph-catenary system, play a critical role in maintaining the structural stability of the catenary support device (CSD) in high-speed railroad. Unfortunately, impact and vibration generated by the interaction between catenary system and high-speed running vehicles will progressively damage SPs, including but not limited to the loose, breakage, and missing of SPs. Current image processing technologies have limited accuracy. Traditional convolutional neural network (CNN)-based systems that directly learn deep features are inefficient in detecting subtle defects (such as fractured and loose SPs). This study presents a novel SP inspection system based on an innovative destruction and reconstruction learning (DRL). In the DRL, a novel cross stage partial network (CSPNet)-based classifier is firstly developed to alleviate the impact of redundant semantic information and optimize gradient regression process. Then, "destruction" in the DRL stream means these partitioned sub-regions in an input image are shuffled by a region shuffling mechanism (RSM); this guides the classifier to pay more attention to the distinguishable regions in the destroyed and original images for a purpose of correct recognition. For "reconstruction", a newly developed position reconstruction network (PRN) is utilized to restore the original spatial distribution of the sub-regions by modeling the semantic correlation among the sub-regions. The proposed DRL can injects more discriminative details to the classifier by jointly training the classification network and PRN. Finally, comprehensive experiments are conducted on CSD datasets captured from real railroad lines, and the results demonstrate that the proposed approach outperforms existing state-of-the-art models yet keeps a reasonable inference speed. [ABSTRACT FROM AUTHOR]

To segment railway track regions in real-time for intrusion detection and improving security, this paper proposes an efficient railway track region segmentation network (ERTNet) based on the encoder–decoder architecture. Firstly, to ensure the lightweight of the encoder, depthwise convolution and the channel shuffle are utilized to construct sandglass-type feature extraction unit. Secondly, a feature-matching-based cross-fusion decoder is utilized to fuse deep and shallow feature maps. Thirdly, the knowledge distillation is employed with large-scale Deeplab v3+ as the teacher model to improve performance. Additionally, a loss function is proposed to penalize pixel points with large offsets. Finally, the ERTNet is validated on the self-built dataset, achieving an MIoU (Mean Intersection over Union) of 92.4% , which is 5.22% improvement over the benchmark model. ERTNet achieves a balance between segmentation accuracy and computational efficiency, requiring only 0.5 M parameters and 0.92 G FLOPs (Floating Point Operations). [Display omitted] • Visual perception is utilized to identify foreign object intrusion into railroad. • Deep learning based ERTNet model is proposed for track region pixel extraction. • ERTNet is built with lightweight neural network and cross-fusion decoder. • Optimizing training modes with knowledge distillation and new loss function. • ERTNet fits real-time needs and helps to improve the safety of rail construction. [ABSTRACT FROM AUTHOR]

With more electrical and electronic equipment applied into the railway system, much more data can be collected and then the big data era of railway is coming. By employing the maximal information coefficient (MIC), the big data analysis of rail equipment accidents is studied to investigate the effect of the updating of rail equipment. The rail equipment accident data set of 25 years (from 1990 to 2014) is separated into three subsets corresponding to the period of the occurrence time of accidents. For every subset, the contributing factors to accident damage, to accident severity, and to accident cause are analyzed, respectively. The results show that the variation trend of the number of rail equipment accidents is more consistent with the variety of railroad service miles rather than carloads. And the factor of highway-rail grade crossings is an important one which accords with the facts. However, a seemingly surprising result is found that there will be more contributing factors to accident severity and to accident causes with more equipment applied into the railway system as time goes on. [ABSTRACT FROM AUTHOR]

Abstract The railway track fasteners play a critical role in fixing the track on the ballast bed. Achieving full automation of the fastener defect detection is significant in terms of ensuring track safety, and reducing maintains cost. In this paper, innovative and intelligent methods using image processing technologies and deep learning networks are proposed. In the first part, the traditional fastener positioning method based on image processing is reconsidered. In addition, a novel fastener defect detection and identification method using Dense-SIFT features is proposed which can achieve a better performance than the methods available in the literature. In the second part, VGG16 is trained for fastener defect detection and recognition. The result demonstrates that it is possible to carry out the defect detection of fasteners with CNN. Finally, Faster R-CNN is used for fastener defect detection to advance detection rate and efficiency. The fastener positioning and recognition can be carried out simultaneously. The time for the defect detection and classification is only one-tenth of the other methods mentioned above. Highlights • The Dense-SIFT, spatial pyramid decomposition and BOVW techniques are applied to the classification and this method achieves classification accuracy of 99.26%. • The DCNN method uses only one network for fastener defects detection and achieves a sound classification accuracy of 97.14%. • Faster R-CNN based method realizes the positioning and classification of the fasteners simultaneously and the time spent by Faster R-CNN is only about 10% of the time spent by the other methods. [ABSTRACT FROM AUTHOR]

China's railway has been experiencing rapid growth recently. The achievement of solar energy for the increasing electricity consumption in the rail sector attracts significant attentions. In this paper, the available solar energy on the covered land and trackside land in the rail itself is assessed for further utilization. The development of the railway electrifications is briefly presented. In the split- and co-phase AC electrifications, AC and DC microgrids are introduced to constitute the solar-powered rail transportation. This approach offers both the on-site access and the local consumption of the available solar energy alongside railways. Focused on the solar power regulation, an individual phase current control (IPCC) strategy is developed without extractions between sequences. This strategy can achieve a flexible current provision for both powering single-phase locomotives and feeding back to the three-phase grid. Finally, the solar-powered rail transportation contributes to a sustainable future of both the rail and solar energy sector and a win-win situation in both the economy and environment in China. • Assessments of the available solar energy along railways for further utilization. • Scenarios for the on-site access and local consumption of solar energy. • Flexible current regulation method with a hybrid current reference. • Solar-powered rail transportation contributes to the rail's sustainable future. [ABSTRACT FROM AUTHOR]

• Multi-scale images speed up method as well as ensure enough and detailed image information. • Low-resolution image contains more edge information with less background noise. • The change law of gray projection curve reflect the location information of intrusion target. • Dynamic candidate region can get rid of most useless information in the image. • Background difference method helps to extract intrusion target by the high-resolution image. This paper focuses on image characteristics of railway monitoring scenes, and proposed a multi-scale image and dynamic candidate region-based automatic detection of foreign targets intruding the railway perimeter. The algorithm first constructs a multi-scale image set for each frame, then obtains a dynamic candidate region of the intrusion target according to the law of gray projection curve variation for the low-resolution image. The foreground image, containing the intrusion target, is obtained using the background differential method for the high-resolution image, then the intruding target is identified before fusion of the results from the two methods. Three railway surveillance videos are used to validate the algorithm comparing with other methods, currently used in the railway scene. Overall, the proposed algorithm guarantees high-accuracy real-time detection of intruding targets, while requiring little computational resources, which is able to achieve 89.3% Precision and 0.028 s/frame with CPU. This technology can reduce the workload of manual inspections, and improve the detection accuracy through the improvement of the model can also reduce the manual review work caused by false alarms. [ABSTRACT FROM AUTHOR]

Trains shuttle in semiopen environments, and the surrounding environment plays an important role in the safety of train operation. The weather is one of the factors that affect the surrounding environment of railways. Under haze conditions, railway monitoring and staff vision could be blurred, threatening railway safety. This paper tackles image dehazing for railways. The contributions of this paper for railway video image dehazing are as follows: (1) this paper proposes an end-to-end residual block-based haze removal method that consists of two subnetworks, namely fine-grained and coarse-grained network can directly generate the clean image from input hazy image, called RID-Net (Railway Image Dehazing Network). (2) The combined loss function (per-pixel loss and perceptual loss functions) is proposed to achieve both low-level features and high-level features so to generate the high-quality restored images. (3) We take the full-reference criterion (PSNR&SSIM), object detection, running time, and sensory vision to evaluate the proposed dehazing method. Experimental results on railway synthesized dataset, benchmark indoor dataset, and real-world dataset demonstrate our method has superior performance compared to the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

According to the International Energy Agency (IEA)'s forecast, China will fully electrify its railway system by 2050. However, the development of electrified railways is limited in the weak areas of China's power grid. To surpass these limitations, we turn our attention to new railway energy sources, among which the most suitable is photovoltaic power generation. To evaluate the feasibility of integrating railway systems and photovoltaic power generation in China, this paper analyzes the geographical conditions and railway layout of China, gives a potential method for evaluating railway asset energy transformation measures and asset energy transformations that are suitable for China's national conditions, and proposes an application of railway system energy transformation on this basis. Then, this method is used to evaluate the potential of a substation section of the Lanxin high-speed railway in China for reference. The results show that the green energy potential and scheduling potential of China's railway assets are great and can effectively alleviate the energy anxiety of China's railway system. [ABSTRACT FROM AUTHOR]