Back to Search
Start Over
A cyber-enabled visual inspection system for rail corrugation
- Source :
- Future Generation Computer Systems. 79:374-382
- Publication Year :
- 2018
- Publisher :
- Elsevier BV, 2018.
-
Abstract
- Rail inspection is one of the most important tasks to guarantee the safety of a railway transportation system, and it requires advanced information technologies (e.g. cyber–physical system and cyber–physical–social system) to build intelligent inspection systems. This paper presents a cyber-enabled visual inspection system for rail corrugation, which includes an on-board image acquisition subsystem and a corrugation identification subsystem. In the corrugation identification subsystem, a track image captured by the on-board image acquisition subsystem is first segmented by the rail locating algorithm based on weighted projection profile (briefly as RLWP). And then each column of the segmented rail image is represented by local frequency features and identified as corrugation line or not by a support vector machine (SVM). Lastly, the rail image is judged as corrugation by integrating the recognized corrugation lines. The experiment results show that RLWP is robust and accurate to localize rail region even for uneven or abominable illumination. Moreover, the precision and recall of the proposed corrugation detection system are 98.47% and 96.50%, respectively. They are 25% and 1% higher than those of traditional methods. At the same time, the detection speed is doubly faster than that of the traditional approach. c
- Subjects :
- Computer Networks and Communications
business.industry
Computer science
020208 electrical & electronic engineering
02 engineering and technology
Track (rail transport)
Visual inspection
Identification (information)
Hardware and Architecture
Line (geometry)
Rail inspection
0202 electrical engineering, electronic engineering, information engineering
020201 artificial intelligence & image processing
Computer vision
Artificial intelligence
business
Projection (set theory)
Software
Subjects
Details
- ISSN :
- 0167739X
- Volume :
- 79
- Database :
- OpenAIRE
- Journal :
- Future Generation Computer Systems
- Accession number :
- edsair.doi...........7fcc7350c835ce86a5fc398f054806d2