Full metadata record
| DC Field | Value | Language |
|---|---|---|
| dc.contributor | Faculty of Construction and Environment | en_US |
| dc.creator | Meng, Ling Qiao | - |
| dc.identifier.uri | https://theses.lib.polyu.edu.hk/handle/200/7575 | - |
| dc.language | English | en_US |
| dc.publisher | Hong Kong Polytechnic University | - |
| dc.rights | All rights reserved | en_US |
| dc.title | Development of biologically and geometrically inspired methods for automatic detection of rail surface defects | en_US |
| dcterms.abstract | The reliability of rail tracks is essential for the safety of rail transit, which plays a significant role in society and the economy. Safety statistics from the U.S. Federal Railroad Administration (FRA, 2002) indicate that track failure-related train accidents resulted in 2,700 derailments and US$441 million in direct costs during the decade from 1992-2002. In Hong Kong incident related to rail cracks occurred on the MTR in January and February of 2011, causing severe service disruptions. Thousands of commuters were affected, as trains were delayed for several hours during the morning rush hour. In the past decade, China has developed a huge high-speed railway network in addition to its existing wide-spread rail system. To ensure better rail maintenance and avoid adverse train incidents, there is an urgent need to develop effective techniques for rail defect detection. In this thesis, a novel multi-feature LEGION algorithm firstly used to process the rail imagery. This algorithm includes an image segmentation step based on the gray value feature and a subsequent image segment grouping step for the extraction of the rail boundary on the basis of the orientation feature. The multi-feature LEGION algorithm help to quickly analyze the imagery and identify the rail boundary and other interesting regions on the image, in this thesis, the interesting region is boundary of railway and defects. Automatic rail boundary extraction plays an important role in the entire automatic process, and rail boundary used as an important reference information in the subsequent rail surface feature extraction. We also use mathematical morphology to extract initial active contour of rail boundary and some defects. Then an improved GAC model is applied to each interesting region within the rail boundary for the refined boundary extraction of these regions. This improved GAC model in this thesis, we choose to use Chan-Vese model, and we also add Power-law and Negative transformation in this model. After comparative analysis of results, we use Power-law Chan-Vese model to detect. All of these methods are described in greater detail in the following sections. An integrated biologically and geometrically inspired approach capable of detecting and recognizing rail source defects from rail images collected in a complex environment is implemented in this dissertation. The algorithms developed in this project will greatly assist rail health analysts in rail surface defect detection, and evaluation. The research and development results obtained in this approach will contribute to the safety of rail transportation and maintenance in Hong Kong and mainland China. The algorithms developed therein can also potentially be transferred to and used in a variety of other industry applications, for example, defect detection on road or tunnel surfaces. The results of this project will also contribute to the advancement of image understanding, scene analysis. | en_US |
| dcterms.extent | xi, 97 pages : illustrations (some color) ; 30 cm | en_US |
| dcterms.isPartOf | PolyU Electronic Theses | en_US |
| dcterms.issued | 2014 | en_US |
| dcterms.educationalLevel | All Master | en_US |
| dcterms.educationalLevel | M.Sc. | en_US |
| dcterms.LCSH | Railroad tracks -- Inspection. | en_US |
| dcterms.LCSH | Hong Kong Polytechnic University -- Dissertations | en_US |
| dcterms.accessRights | restricted access | en_US |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| b2759127x.pdf | For All Users (off-campus access for PolyU Staff & Students only) | 20.02 MB | Adobe PDF | View/Open |
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