Automatic Fastener Classification and Defect Detection in Vision-Based Railway Inspection Systems
- 11 October 2013
- journal article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Transactions on Instrumentation and Measurement
- Vol. 63 (4), 877-888
- https://doi.org/10.1109/tim.2013.2283741
Abstract
The detection of fastener defects is an important task in railway inspection systems, and it is frequently performed to ensure the safety of train traffic. Traditional inspection is usually operated by trained workers who walk along railway lines to search for potential risks. However, the manual inspection is very slow, costly, and dangerous. This paper proposes an automatic visual inspection system for detecting partially worn and completely missing fasteners using probabilistic topic model. Specifically, our method is able to simultaneously model diverse types of fasteners with different orientations and illumination conditions using unlabeled data. To assess the damages, the test fasteners are compared with the trained models and automatically ranked into three levels based on the likelihood probability. The experimental results demonstrate the effectiveness of this method.Keywords
This publication has 21 references indexed in Scilit:
- Component-based track inspection using machine-vision technologyPublished by Association for Computing Machinery (ACM) ,2011
- A GPU-based vision system for real time detection of fastening elements in railway inspectionPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2009
- Object Detection with Discriminatively Trained Part-Based ModelsIEEE Transactions on Pattern Analysis and Machine Intelligence, 2009
- Unsupervised Activity Perception by Hierarchical Bayesian ModelsPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2007
- A FPGA-Based Architecture for Automatic Hexagonal Bolts Detection in Railway MaintenancePublished by Institute of Electrical and Electronics Engineers (IEEE) ,2006
- Autonomous Rail Track Inspection using Vision Based SystemPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2006
- Characterisation of defects in the railhead using ultrasonic surface wavesNDT & E International, 2006
- Filter-based feature selection for rail defect detectionMachine Vision and Applications, 2004
- An embedded system methodology for real-time analysis of railways track profilePublished by Institute of Electrical and Electronics Engineers (IEEE) ,2003
- 10.1162/jmlr.2003.3.4-5.993Applied Physics Letters, 2000