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

被引：57

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.

引用

页数：16

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