Skip to main content
Top

2018 | OriginalPaper | Chapter

Track Surface Defect Detection Based on Image Processing

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

In this paper, computer vision-based methods are presented to detect the rail track surface defects automatically. The detection is the key foundation to inspect and assess railways, and for the operation safety and rail maintenance, railways inspection is the critical task. To achieve this goal, the rail surface edge’s likelihood is investigated, and the Canny edge detector for defects extraction is introduced to guarantee the detection of the rail surface damage accurately. The analysis performed on some image data captured on the field has demonstrated encouraging detection performance on rail track surface defect detection.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Literature
3.
go back to reference Berry A, Nejikovsky B, Gilbert X, Tajaddini A (2008) A high speed video inspection of joint bars using advanced image collection and processing techniques. In: Proceedings of World Congress on railway research, vol 290 Berry A, Nejikovsky B, Gilbert X, Tajaddini A (2008) A high speed video inspection of joint bars using advanced image collection and processing techniques. In: Proceedings of World Congress on railway research, vol 290
4.
go back to reference Li Y, Trinh H, Haas N, Otto C, Pankanti S (2014) Rail component detection, optimization, and assessment for automatic rail track inspection. IEEE T Intell Transp 15(2):760–770CrossRef Li Y, Trinh H, Haas N, Otto C, Pankanti S (2014) Rail component detection, optimization, and assessment for automatic rail track inspection. IEEE T Intell Transp 15(2):760–770CrossRef
5.
go back to reference Feng H, Jiang Z, Xie F, Yang P, Shi J, Chen L (2014) Automatic fastener classification and defect detection in vision-based railway inspection systems. IEEE T Instrum Measur 63(4):877–888CrossRef Feng H, Jiang Z, Xie F, Yang P, Shi J, Chen L (2014) Automatic fastener classification and defect detection in vision-based railway inspection systems. IEEE T Instrum Measur 63(4):877–888CrossRef
6.
go back to reference Yu H (2013) Research on defects inspection technology for rail surface based on machine vision. Master’s thesis, Hunan University (in Chinese) Yu H (2013) Research on defects inspection technology for rail surface based on machine vision. Master’s thesis, Hunan University (in Chinese)
7.
go back to reference Yang D (2014) Railway fastener status detection under non-uniform illumination based on image processing. Master’s thesis, Southwest Jiaotong University (in Chinese) Yang D (2014) Railway fastener status detection under non-uniform illumination based on image processing. Master’s thesis, Southwest Jiaotong University (in Chinese)
8.
go back to reference Nping C (2014) Detection of rail surface defects based on image. Master’s thesis, Central South University (in Chinese) Nping C (2014) Detection of rail surface defects based on image. Master’s thesis, Central South University (in Chinese)
9.
go back to reference Gonzalez RC, Woods RE, Eddins SL (2010) Digital image processing using MATLAB®. McGraw Hill Education Gonzalez RC, Woods RE, Eddins SL (2010) Digital image processing using MATLAB®. McGraw Hill Education
10.
go back to reference Jia M (2005) Design and research of recognition system on railway surface defects. Master’s thesis, Southwest Jiaotong University (in Chinese) Jia M (2005) Design and research of recognition system on railway surface defects. Master’s thesis, Southwest Jiaotong University (in Chinese)
11.
go back to reference Nixon MS, Aguado AS (2012) Feature extraction & image processing for computer vision. Academic Press Nixon MS, Aguado AS (2012) Feature extraction & image processing for computer vision. Academic Press
Metadata
Title
Track Surface Defect Detection Based on Image Processing
Authors
Yuxin Liu
Xiukun Wei
Copyright Year
2018
Publisher
Springer Singapore
DOI
https://doi.org/10.1007/978-981-10-7989-4_23

Premium Partner