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Surface Defects Recognition of Wheel Hub Based on Improved Faster R-CNN

1
School of Mechanical Engineering, Jiangsu University, Zhenjiang 212000, China
2
School of Mechanical Engineering, Anyang Institute of Technology, Anyang 455000, China
3
Training Center, Anyang Institute of Technology, Anyang 455000, China
*
Author to whom correspondence should be addressed.
Electronics 2019, 8(5), 481; https://doi.org/10.3390/electronics8050481
Received: 2 April 2019 / Revised: 23 April 2019 / Accepted: 25 April 2019 / Published: 29 April 2019
(This article belongs to the Special Issue Deep Neural Networks and Their Applications)
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Abstract

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. View Full-Text
Keywords: defects recognition; deep learning; regional proposal network; Faster R-CNN defects recognition; deep learning; regional proposal network; Faster R-CNN
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Sun, X.; Gu, J.; Huang, R.; Zou, R.; Giron Palomares, B. Surface Defects Recognition of Wheel Hub Based on Improved Faster R-CNN. Electronics 2019, 8, 481.

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