Efficient detection of obstacles on tramways using adaptive multilevel thresholding and region growing methods

Wu, Cheng; Xiang, Qiang; Wang, Yiming; Yan, Changsheng; Zhai, Guangyao

doi:10.1177/0954409717720840

Cited by 7 publications

(3 citation statements)

References 11 publications

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“…In [41,42], wavelet transform was compared with Fourier analysis and showed its effectiveness for rail inspection. In [43], an adaptive multilevel thresholding method was applied to segment the ROI of the tramway to estimate the threshold parameters by using a local accumulated histogram. Then the region growing method was adopted to decrease the influence of environmental noise and to predict the trend of the tramway.…”

Section: Related Workmentioning

confidence: 99%

Block Sparse Low-rank Matrix Decomposition based Visual Defect Inspection of Rail Track Surfaces

2019

KSII TIIS

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Low-rank matrix decomposition has shown its capability in many applications such as image in-painting, de-noising, background reconstruction and defect detection etc. In this paper, we consider the texture background of rail track images and the sparse foreground of the defects to construct a low-rank matrix decomposition model with block sparsity for defect inspection of rail tracks, which jointly minimizes the nuclear norm and the 2-1 norm. Similar to ADM, an alternative method is proposed in this study to solve the optimization problem. After image decomposition, the defect areas in the resulting low-rank image will form dark stripes that horizontally cross the entire image, indicating the preciselocations of the defects. Finally, a two-stage defect extraction method is proposed to locate the defect areas. The experimental results of the two datasets show that our algorithm achieved better performance compared with other methods.

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Section: Related Workmentioning

confidence: 99%

Block Sparse Low-rank Matrix Decomposition based Visual Defect Inspection of Rail Track Surfaces

2019

KSII TIIS

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“…Urban rail transit is one of the most popular means of transportation for urban people, and its development speed is also very rapid [1]. Among them, the technology of fully automatic driverless metro train is a hot research content of urban rail transit [2][3][4], and its most key link is the rapid state analysis and emergency treatment when the train encounters obstacles.…”

Section: Introductionmentioning

confidence: 99%

Subway Obstacle Perception and Identification Method Based on Cloud Edge Collaboration

Li¹,

Yan²,

Liu

et al. 2021

Wireless Communications and Mobile Computing

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The traditional analysis method of train obstacle uses isomorphic sensors to obtain the state information and completes detection and identification analysis at the remote end of a network. A single data sample and more processing links will reduce the accuracy and speed analysis for subway encountering obstacles. To solve this problem, this paper proposes a subway obstacle perception and identification method based on cloud edge cooperation. The subway monitoring cloud platform realizes the training and construction of a detection model, and the network edge side completes the situation awareness of track state and real-time action when the train encounters obstacles. Firstly, the railroad track position is detected by cameras, and subway running track is identified by Mask RCNN algorithm to determine the detection area of obstacles in the process of subway train running. At the edge of network, the feature-level fusion of data collected by sensor cluster is carried out to provide reliable data support for detection work. Then, based on the DeepSort and YOLOv3 network models, the subway obstacle detection model is constructed on the subway monitoring cloud platform. Moreover, a trained model is distributed to the network edge side, so as to realize the fast and efficient perception and action of obstacles. Finally, the simulation verification is implemented based on actual collected datasets. Experimental results show that the proposed method has good detection accuracy and efficiency, which maintains 98.9% and 1.43 s for obstacle detection accuracy and recognition time in complex scenes.

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“…Over time, tram traffic and various exogenous variables cause the natural deterioration of the whole tram infrastructure (Oregui et al, 2017). Without an adequate tram asset management strategy, safety and the good structural health condition of the tram system cannot be guaranteed by the infrastructure manager (Talbot, 2013;Wu et al, 2018). Unexpected faults and accidents might have a strong impact on the users, affecting travel time, safety, and the operation of other transportation modes in the city.…”

Section: Introductionmentioning

confidence: 99%

Decision support tool based on multi-source data analysis for the tram wheel-rail interface

Xie

Núñez

Maas³

et al. 2018

IFAC-PapersOnLine

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This paper presents a methodology to support decision making based on the tram wheel-rail interface condition. The methodology relies on the following measurements: tram failure log-files regarding wheel-sliding events, monitored acoustics data and open source weather information. The proposed methodology consists of three stages: 1) data collection and pre-processing, 2) spatial analysis based on clustering, and 3) decision support based on the extracted information. For clustering, the Density-Based Algorithm (DBSCAN) is used for the analysis of wheel-sliding events. Self-organizing maps (SOMs) are employed for the analysis of acoustics data. A real-life case study is used to show how use of the methodology can find interesting hotspots that are candidates for further monitoring and maintenance actions. The measurements were obtained from the tram system in the city of Rotterdam, The Netherlands.

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Efficient detection of obstacles on tramways using adaptive multilevel thresholding and region growing methods

Cited by 7 publications

References 11 publications

Block Sparse Low-rank Matrix Decomposition based Visual Defect Inspection of Rail Track Surfaces

Block Sparse Low-rank Matrix Decomposition based Visual Defect Inspection of Rail Track Surfaces

Subway Obstacle Perception and Identification Method Based on Cloud Edge Collaboration

Decision support tool based on multi-source data analysis for the tram wheel-rail interface

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