Surface Defects Recognition of Wheel Hub Based on Improved Faster R-CNN
Machine vision is one of the key technologies used to perform intelligent manufacturing. In order to improve the recognition rate of multi-class defects in wheel hubs, an improved Faster R-CNN method was proposed. A data set for wheel hub defects was built. This data set consisted of four types of d...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2019-04-01
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| Series: | Electronics |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2079-9292/8/5/481 |
| _version_ | 1797999102929141760 |
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| author | Xiaohong Sun Jinan Gu Rui Huang Rong Zou Benjamin Giron Palomares |
| author_facet | Xiaohong Sun Jinan Gu Rui Huang Rong Zou Benjamin Giron Palomares |
| author_sort | Xiaohong Sun |
| collection | DOAJ |
| description | Machine vision is one of the key technologies used to perform intelligent manufacturing. In order to improve the recognition rate of multi-class defects in wheel hubs, an improved Faster R-CNN method was proposed. A data set for wheel hub defects was built. This data set consisted of four types of defects in 2,412 1080 × 1440 pixels images. Faster R-CNN was modified, trained, verified and tested based on this database. The recognition rate for this proposed method was excellent. The proposed method was compared with the popular R-CNN and YOLOv3 methods showing simpler, faster, and more accurate defect detection, which demonstrates the superiority of the improved Faster R-CNN for wheel hub defects. |
| first_indexed | 2024-04-11T10:59:17Z |
| format | Article |
| id | doaj.art-11525ed29e19479da3708c4bd63cce24 |
| institution | Directory Open Access Journal |
| issn | 2079-9292 |
| language | English |
| last_indexed | 2024-04-11T10:59:17Z |
| publishDate | 2019-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Electronics |
| spelling | doaj.art-11525ed29e19479da3708c4bd63cce242022-12-22T04:28:39ZengMDPI AGElectronics2079-92922019-04-018548110.3390/electronics8050481electronics8050481Surface Defects Recognition of Wheel Hub Based on Improved Faster R-CNNXiaohong Sun0Jinan Gu1Rui Huang2Rong Zou3Benjamin Giron Palomares4School of Mechanical Engineering, Jiangsu University, Zhenjiang 212000, ChinaSchool of Mechanical Engineering, Jiangsu University, Zhenjiang 212000, ChinaSchool of Mechanical Engineering, Jiangsu University, Zhenjiang 212000, ChinaSchool of Mechanical Engineering, Jiangsu University, Zhenjiang 212000, ChinaTraining Center, Anyang Institute of Technology, Anyang 455000, ChinaMachine vision is one of the key technologies used to perform intelligent manufacturing. In order to improve the recognition rate of multi-class defects in wheel hubs, an improved Faster R-CNN method was proposed. A data set for wheel hub defects was built. This data set consisted of four types of defects in 2,412 1080 × 1440 pixels images. Faster R-CNN was modified, trained, verified and tested based on this database. The recognition rate for this proposed method was excellent. The proposed method was compared with the popular R-CNN and YOLOv3 methods showing simpler, faster, and more accurate defect detection, which demonstrates the superiority of the improved Faster R-CNN for wheel hub defects.https://www.mdpi.com/2079-9292/8/5/481defects recognitiondeep learningregional proposal networkFaster R-CNN |
| spellingShingle | Xiaohong Sun Jinan Gu Rui Huang Rong Zou Benjamin Giron Palomares Surface Defects Recognition of Wheel Hub Based on Improved Faster R-CNN Electronics defects recognition deep learning regional proposal network Faster R-CNN |
| title | Surface Defects Recognition of Wheel Hub Based on Improved Faster R-CNN |
| title_full | Surface Defects Recognition of Wheel Hub Based on Improved Faster R-CNN |
| title_fullStr | Surface Defects Recognition of Wheel Hub Based on Improved Faster R-CNN |
| title_full_unstemmed | Surface Defects Recognition of Wheel Hub Based on Improved Faster R-CNN |
| title_short | Surface Defects Recognition of Wheel Hub Based on Improved Faster R-CNN |
| title_sort | surface defects recognition of wheel hub based on improved faster r cnn |
| topic | defects recognition deep learning regional proposal network Faster R-CNN |
| url | https://www.mdpi.com/2079-9292/8/5/481 |
| work_keys_str_mv | AT xiaohongsun surfacedefectsrecognitionofwheelhubbasedonimprovedfasterrcnn AT jinangu surfacedefectsrecognitionofwheelhubbasedonimprovedfasterrcnn AT ruihuang surfacedefectsrecognitionofwheelhubbasedonimprovedfasterrcnn AT rongzou surfacedefectsrecognitionofwheelhubbasedonimprovedfasterrcnn AT benjamingironpalomares surfacedefectsrecognitionofwheelhubbasedonimprovedfasterrcnn |