Multialgorithm Fusion Image Processing for High Speed Railway Dropper Failure–Defect Detection

Tan, Ping; Li, Xufeng; Wu, Zheng‐Guang; Ding, Jingzhong; Ma, Jien; Chen, Yingfeng; Fang, Youtong; Ning, Yong

doi:10.1109/tsmc.2019.2938684

Cited by 42 publications

(20 citation statements)

References 27 publications

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“…Tables 3 and 4, whose structure and aim are described in Section III-E, summarise the papers we found within this Railway Area. Catenary Support Device [95], [96], [97] [98], [99], [100], [101], [102], [103], [104], [105], [106], [107], [108], [109], [110] [111], [112], [113], [114], [115], [116] Pantograph & Arcs [117], [118] [101], [119], [120], [121] [122], [123], [124] Catenary Wires [125], [126], [127], [128], [109] [129]…”

Section: Pantograph and Catenarymentioning

confidence: 99%

“…With respect to Catenary Wires, the droppers have caught the attention of researchers as their failure (e.g., bending, break, deformations, etc.) can lead to unpleasant consequences such as the burnout of the contact wire [125]. Reference [125] proposes a complex system for droppers defect inspection subdivided into (evident) faults and (tiny) defects.…”

Section: Catenary Wiresmentioning

confidence: 99%

“…can lead to unpleasant consequences such as the burnout of the contact wire [125]. Reference [125] proposes a complex system for droppers defect inspection subdivided into (evident) faults and (tiny) defects. If a dropper is identified as faulty, a first system produces that output, otherwise, it is analysed by a second system to identify tiny defects (e.g., little foreign objects, broken strands).…”

Section: Catenary Wiresmentioning

confidence: 99%

“…Among the various object detection methods, the most used approaches are those belonging to the YOLO (mostly Yolov3) family, as they seem to be among the fastest architectures, and those belonging to the regionbased CNN (mostly Faster R-CNN) family, which have been shown to be (generally) more precise even if less fast than the previous [172]. Such models have been used both as defect detectors (e.g., [118], [125]), evaluating defects starting from the original images, and as a first-stage approach in the multistage solutions, to detect the portion of the image related to the components of interest (e.g., [95], [105]). This aspect can be easily derived from Fig.…”

Section: B Response To Rq2mentioning

confidence: 99%

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A Survey on Audio-Video Based Defect Detection Through Deep Learning in Railway Maintenance

et al. 2022

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Within Artificial Intelligence, Deep Learning (DL) represents a paradigm that has been showing unprecedented performance in image and audio processing by supporting or even replacing humans in defect and anomaly detection. The Railway sector is expected to benefit from DL applications, especially in predictive maintenance applications, where smart audio and video sensors can be leveraged yet kept distinct from safety-critical functions. Such separation is crucial, as it allows for improving system dependability with no impact on its safety certification. This is further supported by the development of DL in other transportation domains, such as automotive and avionics, opening for knowledge transfer opportunities and highlighting the potential of such a paradigm in railways. In order to summarize the recent state-of-the-art while inquiring about future opportunities, this paper reviews DL approaches for the analysis of data generated by acoustic and visual sensors in railway maintenance applications that have been published until August 31st, 2021. In this paper, the current state of the research is investigated and evaluated using a structured and systematic method, in order to highlight promising approaches and successful applications, as well as to identify available datasets, current limitations, open issues, challenges, and recommendations about future research directions.

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Section: Pantograph and Catenarymentioning

confidence: 99%

Section: Catenary Wiresmentioning

confidence: 99%

Section: Catenary Wiresmentioning

confidence: 99%

Section: B Response To Rq2mentioning

confidence: 99%

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A Survey on Audio-Video Based Defect Detection Through Deep Learning in Railway Maintenance

et al. 2022

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“…The mobile non-contact detection method is one; a camera or LiDAR (Light Detection And Ranging) is mounted on mobile equipment to collect the information of OCS, then automatically identify particular OCS components in the data, measure specific parameters or detect their fault states. In recent years, deep learning has been widely used in computer vision [4] because of its excellent performance, and many scholars have adopted this method to process the 2D images of OCS to improve the detection efficiency and accuracy [5][6][7]. However, there are few works based on the point cloud.…”

Section: Introductionmentioning

confidence: 99%

LiDAR Point Cloud Recognition of Overhead Catenary System with Deep Learning

Lin

Chen

et al. 2020

Sensors

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High-speed railways have been one of the most popular means of transportation all over the world. As an important part of the high-speed railway power supply system, the overhead catenary system (OCS) directly influences the stable operation of the railway, so regular inspection and maintenance are essential. Now manual inspection is too inefficient and high-cost to fit the requirements for high-speed railway operation, and automatic inspection becomes a trend. The 3D information in the point cloud is useful for geometric parameter measurement in the catenary inspection. Thus it is significant to recognize the components of OCS from the point cloud data collected by the inspection equipment, which promotes the automation of parameter measurement. In this paper, we present a novel method based on deep learning to recognize point clouds of OCS components. The method identifies the context of each single frame point cloud by a convolutional neural network (CNN) and combines some single frame data based on classification results, then inputs them into a segmentation network to identify OCS components. To verify the method, we build a point cloud dataset of OCS components that contains eight categories. The experimental results demonstrate that the proposed method can detect OCS components with high accuracy. Our work can be applied to the real OCS components detection and has great practical significance for OCS automatic inspection.

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