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Unsupervised Saliency Detection of Rail Surface Defects.pdf (3.63 MB)

Unsupervised saliency detection of rail surface defects using stereoscopic images

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journal contribution
posted on 2020-12-07, 11:45 authored by Menghui Niu, Kechen Song, Liming Huang, Qi Wang, Yunhui Yan, Qinggang MengQinggang Meng
Visual information is increasingly recognized as a useful method to detect rail surface defects due to its high efficiency and stability. However, it cannot sufficiently detect a complete defect in the complex background information. The addition of surface profiles can effectively improve this by including a 3-D information of defects. However, in high-speed detection, the traditional 3-D profile acquisition is difficult and separate from the image acquisition, which cannot satisfy the above-mentioned requirements effectively. Therefore, an unsupervised stereoscopic saliency detection method based on a binocular line-scanning system is proposed in this article. This method can simultaneously obtain a highly precise image as well as profile information while also avoids the decoding distortion of the structured light reconstruction method. In our method, a global low-rank nonnegative reconstruction algorithm with a background constraint is proposed. Unlike the low-rank recovery model, the algorithm has a more comprehensive low rank and background clustering properties. Furthermore, outlier detection based on the geometric properties of the rail surface is also proposed in this method. Finally, the image saliency results and depth outlier detection results are associated with the collaborative fusion, and a dataset (RSDDS-113) containing the rail surface defects is established for the experimental verification. The experimental results demonstrate that our method can obtain a mean absolute error of 0.09 and area under the ROC curve of 0.94, better than 15 state-of-the-art algorithms.

Funding

National Natural Science Foundation of China under Grant 51805078 and Grant 51374063

National Key Research and Development Program of China under Grant 2017YFB0304200

History

School

  • Science

Department

  • Computer Science

Published in

IEEE Transactions on Industrial Informatics

Volume

17

Issue

3

Pages

2271 - 2281

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Version

  • AM (Accepted Manuscript)

Rights holder

© IEEE

Publisher statement

© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

Acceptance date

2020-06-08

Publication date

2020-06-23

Copyright date

2020

ISSN

1551-3203

eISSN

1941-0050

Language

  • en

Depositor

Prof Qinggang Meng. Deposit date: 6 December 2020

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