Standard

Real Time Identification of Railway Track Surface Faults using Canny Edge Detector and 2D Discrete Wavelet Transform. / Shah, Ali Akbar; Chowdhry, Bhawani S.; Memon, Tayab D.; Kalwar, Imtiaz H.; Ware, Andrew.

In: Annals of Emerging Technologies in Computing, Vol. 4, No. 2, 01.04.2020, p. 53-60.

Research output: Contribution to journalArticle

Harvard

Shah, AA, Chowdhry, BS, Memon, TD, Kalwar, IH & Ware, A 2020, 'Real Time Identification of Railway Track Surface Faults using Canny Edge Detector and 2D Discrete Wavelet Transform', Annals of Emerging Technologies in Computing, vol. 4, no. 2, pp. 53-60. https://doi.org/10.33166/AETiC.2020.02.005

APA

Shah, A. A., Chowdhry, B. S., Memon, T. D., Kalwar, I. H., & Ware, A. (2020). Real Time Identification of Railway Track Surface Faults using Canny Edge Detector and 2D Discrete Wavelet Transform. Annals of Emerging Technologies in Computing, 4(2), 53-60. https://doi.org/10.33166/AETiC.2020.02.005

Vancouver

Shah AA, Chowdhry BS, Memon TD, Kalwar IH, Ware A. Real Time Identification of Railway Track Surface Faults using Canny Edge Detector and 2D Discrete Wavelet Transform. Annals of Emerging Technologies in Computing. 2020 Apr 1;4(2):53-60. https://doi.org/10.33166/AETiC.2020.02.005

Author

Shah, Ali Akbar ; Chowdhry, Bhawani S. ; Memon, Tayab D. ; Kalwar, Imtiaz H. ; Ware, Andrew. / Real Time Identification of Railway Track Surface Faults using Canny Edge Detector and 2D Discrete Wavelet Transform. In: Annals of Emerging Technologies in Computing. 2020 ; Vol. 4, No. 2. pp. 53-60.

BibTeX

@article{91009b70f77a4575a42f636bf3037df4,
title = "Real Time Identification of Railway Track Surface Faults using Canny Edge Detector and 2D Discrete Wavelet Transform",
abstract = "Usually, railway accidents are caused by train derailment, the mechanical failure of tracks, such as broken rails often caused by lack of railway condition monitoring. Such monitoring could identify track surface faults, such as squats, that act as a catalyst for the track to crack and ultimately break. The research presented in this paper enables real-time identification of railway track faults using image processing techniques such as Canny edge detection and 2D discrete wavelet transformation. The Canny edge detection outperforms traditional track damage detection techniques including Axle Based Acceleration using Inertial Measurement Units and is as reliable as Fiber Bragg Grating. The Canny edge detection employed can identify squats in real-time owing to its specific threshold amplitude using a camera module mounted on a specially designed handheld Track Recording Vehicle (TRV). The 2D discrete wavelet transformation validates the insinuation of the Canny edge detector regarding track damage and furthermore determines damage severity, by applying high sub band frequency filter. The entire algorithm works on a Raspberry Pi 3 B+ utilizing an OpenCV API. When tested using an actual rail track, the algorithm proved reliable at determining track surface damage in real-time. Although wavelet transformation performs better than Canny edge detection in terms of determining the severity of track surface damage, it has processing overheads that become a bottleneck in real-time. To overcome this deficiency a very effective two-stage process has been developed.",
keywords = "Railway condition monitoring, real-time, Canny edge detection, Wavelet transformation, squats",
author = "Shah, {Ali Akbar} and Chowdhry, {Bhawani S.} and Memon, {Tayab D.} and Kalwar, {Imtiaz H.} and Andrew Ware",
year = "2020",
month = "4",
day = "1",
doi = "10.33166/AETiC.2020.02.005",
language = "English",
volume = "4",
pages = "53--60",
journal = "Annals of Emerging Technologies in Computing",
issn = "2516-0281",
publisher = "International Association for Educators and Researchers (IAER)",
number = "2",

}

RIS

TY - JOUR

T1 - Real Time Identification of Railway Track Surface Faults using Canny Edge Detector and 2D Discrete Wavelet Transform

AU - Shah, Ali Akbar

AU - Chowdhry, Bhawani S.

AU - Memon, Tayab D.

AU - Kalwar, Imtiaz H.

AU - Ware, Andrew

PY - 2020/4/1

Y1 - 2020/4/1

N2 - Usually, railway accidents are caused by train derailment, the mechanical failure of tracks, such as broken rails often caused by lack of railway condition monitoring. Such monitoring could identify track surface faults, such as squats, that act as a catalyst for the track to crack and ultimately break. The research presented in this paper enables real-time identification of railway track faults using image processing techniques such as Canny edge detection and 2D discrete wavelet transformation. The Canny edge detection outperforms traditional track damage detection techniques including Axle Based Acceleration using Inertial Measurement Units and is as reliable as Fiber Bragg Grating. The Canny edge detection employed can identify squats in real-time owing to its specific threshold amplitude using a camera module mounted on a specially designed handheld Track Recording Vehicle (TRV). The 2D discrete wavelet transformation validates the insinuation of the Canny edge detector regarding track damage and furthermore determines damage severity, by applying high sub band frequency filter. The entire algorithm works on a Raspberry Pi 3 B+ utilizing an OpenCV API. When tested using an actual rail track, the algorithm proved reliable at determining track surface damage in real-time. Although wavelet transformation performs better than Canny edge detection in terms of determining the severity of track surface damage, it has processing overheads that become a bottleneck in real-time. To overcome this deficiency a very effective two-stage process has been developed.

AB - Usually, railway accidents are caused by train derailment, the mechanical failure of tracks, such as broken rails often caused by lack of railway condition monitoring. Such monitoring could identify track surface faults, such as squats, that act as a catalyst for the track to crack and ultimately break. The research presented in this paper enables real-time identification of railway track faults using image processing techniques such as Canny edge detection and 2D discrete wavelet transformation. The Canny edge detection outperforms traditional track damage detection techniques including Axle Based Acceleration using Inertial Measurement Units and is as reliable as Fiber Bragg Grating. The Canny edge detection employed can identify squats in real-time owing to its specific threshold amplitude using a camera module mounted on a specially designed handheld Track Recording Vehicle (TRV). The 2D discrete wavelet transformation validates the insinuation of the Canny edge detector regarding track damage and furthermore determines damage severity, by applying high sub band frequency filter. The entire algorithm works on a Raspberry Pi 3 B+ utilizing an OpenCV API. When tested using an actual rail track, the algorithm proved reliable at determining track surface damage in real-time. Although wavelet transformation performs better than Canny edge detection in terms of determining the severity of track surface damage, it has processing overheads that become a bottleneck in real-time. To overcome this deficiency a very effective two-stage process has been developed.

KW - Railway condition monitoring

KW - real-time

KW - Canny edge detection

KW - Wavelet transformation

KW - squats

U2 - 10.33166/AETiC.2020.02.005

DO - 10.33166/AETiC.2020.02.005

M3 - Article

VL - 4

SP - 53

EP - 60

JO - Annals of Emerging Technologies in Computing

JF - Annals of Emerging Technologies in Computing

SN - 2516-0281

IS - 2

ER -

ID: 3768109