Research on Gear Appearance Defect Recognition Based on Improved Faster R-CNN

Authors

Weixi Ji, 1. School of Mechanical Engineering, Jiangnan University, Wuxi 214122, China; ;2. Jiangsu Provincial Key Laboratory of Food Manufacturing Equipment, Wuxi 214122, China;
Du Meng, 1. School of Mechanical Engineering, Jiangnan University, Wuxi 214122, China; ;
Peng Wei, 1. School of Mechanical Engineering, Jiangnan University, Wuxi 214122, China; ;2. Jiangsu Provincial Key Laboratory of Food Manufacturing Equipment, Wuxi 214122, China;
Xu Jie, 1. School of Mechanical Engineering, Jiangnan University, Wuxi 214122, China; ;2. Jiangsu Provincial Key Laboratory of Food Manufacturing Equipment, Wuxi 214122, China;

Abstract

Abstract: In order to achieve automatic identification of gear appearance defects and improve the qualification rate of gear products, aiming at the generalization of traditional defect recognition algorithms and the time-consuming of manual features extraction, this paper proposes an improved gear flaw detection algorithm for Faster R-CNN. VGG-2CF network is designed to improve the ability to identify smaller targets. Introducing AM-Softmax loss function is introduced to reduce the intra-class variation and optimize the inter-class difference. Combining with F-measure in machine learning algorithm, an AMF-Softmax loss function is proposed to solve the problem of data imbalance. The experimental results show the improved model proposed in the paper has a high recognition rate and is suitable for automatic detection of gear appearance.

Recommended Citation

Ji, Weixi; Meng, Du; Wei, Peng; and Jie, Xu (2019) "Research on Gear Appearance Defect Recognition Based on Improved Faster R-CNN," Journal of System Simulation: Vol. 31: Iss. 11, Article 2.
DOI: 10.16182/j.issn1004731x.joss.19-0545
Available at: https://dc-china-simulation.researchcommons.org/journal/vol31/iss11/2

First Page

2198

Revised Date

2019-10-10

DOI Link

https://doi.org/10.16182/j.issn1004731x.joss.19-0545

Last Page

2205

CLC

TP391.4

Recommended Citation

Ji Weixi, Du Meng, Peng Wei, Xu Jie. Research on Gear Appearance Defect Recognition Based on Improved Faster R-CNN[J]. Journal of System Simulation, 2019, 31(11): 2198-2205.

DOI

10.16182/j.issn1004731x.joss.19-0545