This paper is published in Volume-3, Issue-4, 2017
Area
Image Processing
Author
Aliza Raza Rizvi, Pervez Rauf Khan, Dr. Shafeeq Ahmad
Org/Univ
Azad Institute of Engineering & Technology, Lucknow, Uttar Pradesh, India
Pub. Date
31 July, 2017
Paper ID
V3I4-1266
Publisher
Keywords
Railway Track, Cracks, Manual inspection, Image Processing, Computer Vision.

Citationsacebook

IEEE
Aliza Raza Rizvi, Pervez Rauf Khan, Dr. Shafeeq Ahmad. Crack Detection in Railway Track Using Image Processing, International Journal of Advance Research, Ideas and Innovations in Technology, www.IJARIIT.com.

APA
Aliza Raza Rizvi, Pervez Rauf Khan, Dr. Shafeeq Ahmad (2017). Crack Detection in Railway Track Using Image Processing. International Journal of Advance Research, Ideas and Innovations in Technology, 3(4) www.IJARIIT.com.

MLA
Aliza Raza Rizvi, Pervez Rauf Khan, Dr. Shafeeq Ahmad. "Crack Detection in Railway Track Using Image Processing." International Journal of Advance Research, Ideas and Innovations in Technology 3.4 (2017). www.IJARIIT.com.

Abstract

Computer vision can provide many potential advantages over manual methods of railway track inspection. Great levels of performance can be achieved through the automation of inspection using computer vision systems, as they allow scalable, quick, and cost-effective solutions to tasks otherwise unsuited to humans. At a minimum, railway track components can be objectively and quantitatively inspected, as the system does not suffer from fatigue or the subjectivity inherent with human inspectors. The digital nature of the data collection involved with a computer vision based method, archiving inspection results and trending of the data becomes feasible, leading to more advanced failure prediction models for maintenance scheduling and a more thorough understanding of railway track structure. In this research paper, a computer vision based method is presented. A system has been suggested which can periodically take images of the railway tracks and compared with the existing database of non-faulty track images on a continuous basis. If a fault arises in the track section, the system will automatically detect the fault and necessary actions can be taken, to avoid any mis-happening.