Defect Detection of Pandrol Track Fastener Based on Local Depth Feature Fusion Network
There are three main problems in track fastener defect detection based on image: (1) The number of abnormal fastener pictures is scarce, and supervised learning detection model is difficult to establish. (2) The potential data features obtained by different feature extraction methods are different....
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2021/6687146 |
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| author | Zhaomin Lv Anqi Ma Xingjie Chen Shubin Zheng |
| author_facet | Zhaomin Lv Anqi Ma Xingjie Chen Shubin Zheng |
| author_sort | Zhaomin Lv |
| collection | DOAJ |
| description | There are three main problems in track fastener defect detection based on image: (1) The number of abnormal fastener pictures is scarce, and supervised learning detection model is difficult to establish. (2) The potential data features obtained by different feature extraction methods are different. Some methods focus on edge features, and some methods focus on texture features. Different features have different detection capabilities, and these features are not effectively fused and utilized. (3) The detection of the track fastener clip will be interfered by the track fastener bolt subimage. Aiming at the above three problems, a method for track fastener defects detection based on Local Deep Feature Fusion Network (LDFFN) is proposed. Firstly, the track fastener image segmentation method is used to obtain the track fastener clip subimage, which can effectively reduce the interference of bolt subimage features on the track fastener clip detection. Secondly, the edge features and texture features of track fastener clip subimages are extracted by Autoencoder (AE) and Restricted Boltzmann Machine (RBM), and the features are fused. Finally, the similarity measurement method Mahalanobis Distance (MD) is used to detect defects in track fasteners. The effectiveness of the proposed method is verified by real Pandrol track fastener images. |
| format | Article |
| id | doaj-art-e0593917a30244eba357a920a6ee1b92 |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-e0593917a30244eba357a920a6ee1b922025-02-03T06:08:08ZengWileyComplexity1076-27871099-05262021-01-01202110.1155/2021/66871466687146Defect Detection of Pandrol Track Fastener Based on Local Depth Feature Fusion NetworkZhaomin Lv0Anqi Ma1Xingjie Chen2Shubin Zheng3School of Urban Rail Transportation, Shanghai University of Engineering Science, Shanghai 201620, ChinaSchool of Urban Rail Transportation, Shanghai University of Engineering Science, Shanghai 201620, ChinaSchool of Urban Rail Transportation, Shanghai University of Engineering Science, Shanghai 201620, ChinaSchool of Urban Rail Transportation, Shanghai University of Engineering Science, Shanghai 201620, ChinaThere are three main problems in track fastener defect detection based on image: (1) The number of abnormal fastener pictures is scarce, and supervised learning detection model is difficult to establish. (2) The potential data features obtained by different feature extraction methods are different. Some methods focus on edge features, and some methods focus on texture features. Different features have different detection capabilities, and these features are not effectively fused and utilized. (3) The detection of the track fastener clip will be interfered by the track fastener bolt subimage. Aiming at the above three problems, a method for track fastener defects detection based on Local Deep Feature Fusion Network (LDFFN) is proposed. Firstly, the track fastener image segmentation method is used to obtain the track fastener clip subimage, which can effectively reduce the interference of bolt subimage features on the track fastener clip detection. Secondly, the edge features and texture features of track fastener clip subimages are extracted by Autoencoder (AE) and Restricted Boltzmann Machine (RBM), and the features are fused. Finally, the similarity measurement method Mahalanobis Distance (MD) is used to detect defects in track fasteners. The effectiveness of the proposed method is verified by real Pandrol track fastener images.http://dx.doi.org/10.1155/2021/6687146 |
| spellingShingle | Zhaomin Lv Anqi Ma Xingjie Chen Shubin Zheng Defect Detection of Pandrol Track Fastener Based on Local Depth Feature Fusion Network Complexity |
| title | Defect Detection of Pandrol Track Fastener Based on Local Depth Feature Fusion Network |
| title_full | Defect Detection of Pandrol Track Fastener Based on Local Depth Feature Fusion Network |
| title_fullStr | Defect Detection of Pandrol Track Fastener Based on Local Depth Feature Fusion Network |
| title_full_unstemmed | Defect Detection of Pandrol Track Fastener Based on Local Depth Feature Fusion Network |
| title_short | Defect Detection of Pandrol Track Fastener Based on Local Depth Feature Fusion Network |
| title_sort | defect detection of pandrol track fastener based on local depth feature fusion network |
| url | http://dx.doi.org/10.1155/2021/6687146 |
| work_keys_str_mv | AT zhaominlv defectdetectionofpandroltrackfastenerbasedonlocaldepthfeaturefusionnetwork AT anqima defectdetectionofpandroltrackfastenerbasedonlocaldepthfeaturefusionnetwork AT xingjiechen defectdetectionofpandroltrackfastenerbasedonlocaldepthfeaturefusionnetwork AT shubinzheng defectdetectionofpandroltrackfastenerbasedonlocaldepthfeaturefusionnetwork |