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

被引：56
|
作者：
Sun, Xiaohong [1 ,2 ]
Gu, Jinan [1 ]
Huang, Rui [1 ]
Zou, Rong [1 ]
Palomares, Benjamin Giron [3 ]
机构：
[1] Jiangsu Univ, Sch Mech Engn, Zhenjiang 212000, Jiangsu, Peoples R China
[2] Anyang Inst Technol, Sch Mech Engn, Anyang 455000, Peoples R China
[3] Anyang Inst Technol, Training Ctr, Anyang 455000, Peoples R China
来源：
ELECTRONICS | 2019年 / 8卷 / 05期
基金：
中国国家自然科学基金;
关键词：
defects recognition; deep learning; regional proposal network; Faster R-CNN; SYSTEM;
D O I：
10.3390/electronics8050481
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
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 x 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.
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收藏
页数：16
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