NDLI: Railway Fastener Defects Detection Using Gaussian Mixture Deformable Part Model

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Railway Fastener Defects Detection Using Gaussian Mixture Deformable Part Model

Content Provider	Scilit
Author	He, Biao Hou, Yun Xiong, Ying Li, Bailin
Copyright Year	2019
Description	Journal: Journal of Physics: Conference Series This paper addressed the problem of detecting the completely missing and partly missing railway fasteners in the collected images. A Gaussian mixture deformable part model (GMDPM) algorithm was proposed using histogram of oriented gradient (HOG) features. The fastener template was divided into four parts considering the shape of the fastener, and seed points were uniformly sampled along the fastener's shape contour. The part and the seed point deformation were defined to fit the deformation of the fastener. Each seed point template in the part model was solved iteratively by using Gaussian mixture model (GMM) with an expectation-maximization algorithm. The results reveal that the proposed method achieves good performance, especially when the illumination difference is large and the fastener is partially occluded or has slight shape deformation.
Related Links	https://iopscience.iop.org/article/10.1088/1742-6596/1302/2/022102/pdf
ISSN	17426588
e-ISSN	17426596
DOI	10.1088/1742-6596/1302/2/022102
Journal	Journal of Physics: Conference Series
Issue Number	2
Volume Number	1302
Language	English
Publisher	IOP Publishing
Publisher Date	2019-08-01
Access Restriction	Open
Subject Keyword	Journal: Journal of Physics: Conference Series Transportation Science and Technology Using Gaussian Mixture
Content Type	Text
Resource Type	Article
Subject	Physics and Astronomy

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