Classification of Faults in Railway Ties Using Computer Vision and Machine Learning
| dc.contributor.author | Kulkarni, Amruta Kiran | en |
| dc.contributor.committeechair | Kochersberger, Kevin B. | en |
| dc.contributor.committeemember | Parikh, Devi | en |
| dc.contributor.committeemember | Abbott, A. Lynn | en |
| dc.contributor.department | Electrical and Computer Engineering | en |
| dc.date.accessioned | 2018-12-23T07:00:35Z | en |
| dc.date.available | 2018-12-23T07:00:35Z | en |
| dc.date.issued | 2017-06-30 | en |
| dc.description.abstract | This work focuses on automated classification of railway ties based on their condition using aerial imagery. Four approaches are explored and compared to achieve this goal - handcrafted features, HOG features, transfer learning and proposed CNN architecture. Mean test accuracy per class and Quadratic Weighted Kappa score are used as performance metrics, particularly suited for the ordered classification in this work. Transfer learning approach outperforms the handcrafted features and HOG features by a significant margin. The proposed CNN architecture caters to the unique nature of the railway tie images and their defects. The performance of this approach is superior to the handcrafted and HOG features. It also shows a significant reduction in the number of parameters as compared to the transfer learning approach. Data augmentation boosts the performance of all approaches. The problem of label noise is also analyzed. The techniques proposed in this work will help in reducing the time, cost and dependency on experts involved in traditional railway tie inspections and will facilitate efficient documentation and planning for maintenance of railway ties. | en |
| dc.description.abstractgeneral | Railway tracks and their components need to be frequently inspected for defects or design non-compliances. Manual inspections involve long hours, high cost and dependency on the availability of experts. Previous efforts to automate the inspection of the railway track inspections are focused towards either other components of the railway track or towards using custom designed ground vehicles. This work presents four approaches to automate the inspection of wooden railway ties by categorizing them into one of three categories based on their condition. Images of the track are taken by an aerial vehicle, in which the track is left untouched. The techniques of computer vision and machine learning used in this work outperform the baselines. The efforts are directed towards making the algorithm learn from the labeled data. The labeled data is also artificially enlarged and enriched, which boosts the performance of the classifiers. The performance metrics used to evaluate the classification approaches are particularly suited for the task at hand. The problem of inconsistency in labeling between two human labelers is analyzed. Potential further directions for research are also identified. | en |
| dc.description.degree | Master of Science | en |
| dc.format.medium | ETD | en |
| dc.identifier.other | vt_gsexam:12180 | en |
| dc.identifier.uri | http://hdl.handle.net/10919/86522 | en |
| dc.publisher | Virginia Tech | en |
| dc.rights | In Copyright | en |
| dc.rights.uri | http://rightsstatements.org/vocab/InC/1.0/ | en |
| dc.subject | Computer Vision | en |
| dc.subject | Machine Learning | en |
| dc.subject | Railway Ties | en |
| dc.title | Classification of Faults in Railway Ties Using Computer Vision and Machine Learning | en |
| dc.type | Thesis | en |
| thesis.degree.discipline | Computer Engineering | en |
| thesis.degree.grantor | Virginia Polytechnic Institute and State University | en |
| thesis.degree.level | masters | en |
| thesis.degree.name | Master of Science | en |
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