The Identification and Assessment of Rail Corrugation Based on Computer Vision
The identification and assessment of rail corrugation are two of the essential tasks of daily railway inspection to guarantee the safety of train operation and promote the development of an efficient maintenance strategy. In view of the requirements for automatic identification and smart decision-ma...
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doaj-c3b14c5131cc4e03962afe635c1296e32020-11-25T02:12:18ZengMDPI AGApplied Sciences2076-34172019-09-01918391310.3390/app9183913app9183913The Identification and Assessment of Rail Corrugation Based on Computer VisionDehua Wei0Xiukun Wei1Yuxin Liu2Limin Jia3Wenqiang Zhang4School of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044, ChinaState Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing 100044, ChinaSchool of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044, ChinaState Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing 100044, ChinaBeijing Mass Transit Railway Operation Corporation LTD, Beijing 100044, ChinaThe identification and assessment of rail corrugation are two of the essential tasks of daily railway inspection to guarantee the safety of train operation and promote the development of an efficient maintenance strategy. In view of the requirements for automatic identification and smart decision-making, computer vision-based rail corrugation identification and assessment methods are presented in this paper. Firstly, an improved Spatial Pyramid Matching (SPM) model, integrating multi-features and locality-constrained linear coding (IMFLLC), is proposed for rail corrugation identification. After that, an innovative period estimation method for rail corrugation is proposed based on the frequency domain analysis of each column in the corrugation region. Finally, the severity of the rail corrugation is assessed with the help of the wear saliency calculation and fuzzy theory. The experiment results demonstrate that the proposed corrugation identification method achieves a higher precision rate and recall rate than those of traditional methods, reaching 99.67% and 98.34%, respectively. Besides, the validity and feasibility of the proposed methods for the rail corrugation period estimation and severity assessment are also investigated.https://www.mdpi.com/2076-3417/9/18/3913rail corrugation identificationperiod estimationseverity assessmentcomputer vision |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Dehua Wei Xiukun Wei Yuxin Liu Limin Jia Wenqiang Zhang |
spellingShingle |
Dehua Wei Xiukun Wei Yuxin Liu Limin Jia Wenqiang Zhang The Identification and Assessment of Rail Corrugation Based on Computer Vision Applied Sciences rail corrugation identification period estimation severity assessment computer vision |
author_facet |
Dehua Wei Xiukun Wei Yuxin Liu Limin Jia Wenqiang Zhang |
author_sort |
Dehua Wei |
title |
The Identification and Assessment of Rail Corrugation Based on Computer Vision |
title_short |
The Identification and Assessment of Rail Corrugation Based on Computer Vision |
title_full |
The Identification and Assessment of Rail Corrugation Based on Computer Vision |
title_fullStr |
The Identification and Assessment of Rail Corrugation Based on Computer Vision |
title_full_unstemmed |
The Identification and Assessment of Rail Corrugation Based on Computer Vision |
title_sort |
identification and assessment of rail corrugation based on computer vision |
publisher |
MDPI AG |
series |
Applied Sciences |
issn |
2076-3417 |
publishDate |
2019-09-01 |
description |
The identification and assessment of rail corrugation are two of the essential tasks of daily railway inspection to guarantee the safety of train operation and promote the development of an efficient maintenance strategy. In view of the requirements for automatic identification and smart decision-making, computer vision-based rail corrugation identification and assessment methods are presented in this paper. Firstly, an improved Spatial Pyramid Matching (SPM) model, integrating multi-features and locality-constrained linear coding (IMFLLC), is proposed for rail corrugation identification. After that, an innovative period estimation method for rail corrugation is proposed based on the frequency domain analysis of each column in the corrugation region. Finally, the severity of the rail corrugation is assessed with the help of the wear saliency calculation and fuzzy theory. The experiment results demonstrate that the proposed corrugation identification method achieves a higher precision rate and recall rate than those of traditional methods, reaching 99.67% and 98.34%, respectively. Besides, the validity and feasibility of the proposed methods for the rail corrugation period estimation and severity assessment are also investigated. |
topic |
rail corrugation identification period estimation severity assessment computer vision |
url |
https://www.mdpi.com/2076-3417/9/18/3913 |
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