Detecting Deformation on Pantograph Contact Strip of Railway Vehicle on Image Processing and Deep Learning

Na, Kyungmin; Lee, Ki-Won; Shin, Seunghyup; Kim, Hyungchul

doi:10.3390/app10238509

Cited by 14 publications

(6 citation statements)

References 20 publications

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“…The key of image detection method is image processing. In [156], region-based-convolutional neural network (R-CNN) was used to extract the ROI (Regions of Interest) of the slide plate, as the wear size of the slide plate was detected based on the least square method and laser line. Aydin et al [157] provide a method based on image processing and pattern recognition for online monitoring of the pantograph-catenary system.…”

Section: Image Detectionmentioning

confidence: 99%

Pantograph–catenary electrical contact system of high-speed railways: recent progress, challenges, and outlooks

Dong

et al. 2022

Rail. Eng. Science

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As the unique power entrance, the pantograph–catenary electrical contact system maintains the efficiency and reliability of power transmission for the high-speed train. Along with the fast development of high-speed railways all over the world, some commercialized lines are built for covering the remote places under harsh environment, especially in China; these environmental elements including wind, sand, rain, thunder, ice and snow need to be considered during the design of the pantograph–catenary system. The pantograph–catenary system includes the pantograph, the contact wire and the interface—pantograph slide. As the key component, this pantograph slide plays a critical role in reliable power transmission under dynamic condition. The fundamental material characteristics of the pantograph slide and contact wire such as electrical conductivity, impact resistance, wear resistance, etc., directly determine the sliding electrical contact performance of the pantograph–catenary system; meanwhile, different detection methods of the pantograph–catenary system are crucial for the reliability of service and maintenance. In addition, the challenges brought from extreme operational conditions are discussed, taking the Sichuan–Tibet Railway currently under construction as a special example with the high-altitude climate. The outlook for developing the ultra-high-speed train equipped with the novel pantograph–catenary system which can address the harsher operational environment is also involved. This paper has provided a comprehensive review of the high-speed railway pantograph–catenary systems, including its progress, challenges, outlooks in the history and future.

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Section: Image Detectionmentioning

confidence: 99%

Pantograph–catenary electrical contact system of high-speed railways: recent progress, challenges, and outlooks

Dong

et al. 2022

Rail. Eng. Science

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“…In [ 9 ], the research presented a diagnosis of the hydraulic axial pump system using a conversion of signals to images that is conducted via continuous wavelet transform, respectively, the extraction of the feature from the transformed time–frequency images. In [ 10 ], the authors approached an image-processing solution and deep learning to detect deformation on pantograph contact strip of railway vehicles. In [ 11 ], the authors presented a low-cost image-processing solution based on Open-Source Computer Vision Library (OpenCV) that is used to detect defects in automotive parts manufacturing, particularly defects as faulty, missing, or extra pins, faulty clips, board cracks, and electronic control units (ECUs).…”

Section: Related Researchmentioning

confidence: 99%

Approaching OPC UA Publish–Subscribe in the Context of UDP-Based Multi-Channel Communication and Image Transmission

Ioana

Burlacu

Korodi

2021

Sensors

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The Open Platform Communication Unified Architecture (OPC UA) protocol is a key enabler of Industry 4.0 and Industrial Internet of Things (IIoT). OPC UA is already accepted by the industry and its presence is expected to reach more and more fields, applications, and hierarchical levels. Advances within the latest specifications are providing the opportunity to extend the capabilities and the applicability of the protocol, targeting better performances in terms of data volumes, speed, availability, footprint, and security. Continuing previous researches focusing on the publish–subscribe (pub/sub) mechanism and real-time constraints, the current study aims to consider higher data-volumes, approach the multi-channel User Datagram Protocol (UDP)-based communication, and analyze the robustness of the developed mechanism in the context of long-term data transmission. Consequently, the research proposes to extend the applicability of the OPC UA in the context of image transmission. Although highly needed, the image transmission after processing is currently beyond the reach of OPC UA or other legacy industrial protocols, being considered as a separate fraction in the industrial environment. The concept and developments are applied considering both the end-of-line industrial manufacturing process in the automotive sector and the car-to-infrastructure communication. Without special hardware constraints, the obtained results are proven to be appreciable, opening various future perspectives for image transmission using OPC UA.

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“…Refs. [ 12 , 13 , 14 , 15 ] and others combine deep learning and image processing to greatly improve the stability of pantograph detection by a single reliance on neural networks, but there are still major limitations in complex scenes. The proposed methods of [ 16 , 17 , 18 ] are not very practical for complex scenes and external interference, and the complex scenes that can be overcome are very limited.…”

Section: Introductionmentioning

confidence: 99%

Pantograph Detection Algorithm with Complex Background and External Disturbances

Tan

Cui

et al. 2022

Sensors

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As an important equipment for high-speed railway (HSR) to obtain electric power from outside, the state of the pantograph will directly affect the operation safety of HSR. In order to solve the problems that the current pantograph detection method is easily affected by the environment, cannot effectively deal with the interference of external scenes, has a low accuracy rate and can hardly meet the actual operation requirements of HSR, this study proposes a pantograph detection algorithm. The algorithm mainly includes three parts: the first is to use you only look once (YOLO) V4 to detect and locate the pantograph region in real-time; the second is the blur and dirt detection algorithm for the external interference directly affecting the high-speed camera (HSC), which leads to the pantograph not being detected; the last is the complex background detection algorithm for the external complex scene “overlapping” with the pantograph when imaging, which leads to the pantograph not being recognized effectively. The dirt and blur detection algorithm combined with blob detection and improved Brenner method can accurately evaluate the dirt or blur of HSC, and the complex background detection algorithm based on grayscale and vertical projection can greatly reduce the external scene interference during HSR operation. The algorithm proposed in this study was analyzed and studied on a large number of video samples of HSR operation, and the precision on three different test samples reached 99.92%, 99.90% and 99.98%, respectively. Experimental results show that the algorithm proposed in this study has strong environmental adaptability and can effectively overcome the effects of complex background and external interference on pantograph detection, and has high practical application value.

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Detecting Deformation on Pantograph Contact Strip of Railway Vehicle on Image Processing and Deep Learning

Cited by 14 publications

References 20 publications

Pantograph–catenary electrical contact system of high-speed railways: recent progress, challenges, and outlooks

Pantograph–catenary electrical contact system of high-speed railways: recent progress, challenges, and outlooks

Approaching OPC UA Publish–Subscribe in the Context of UDP-Based Multi-Channel Communication and Image Transmission

Pantograph Detection Algorithm with Complex Background and External Disturbances

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