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

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

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.

Key words: gear defect recognition, Faster R-CNN, VGG-2CF, AMF-Softmax loss function

CLC Number:

TP391.4

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.

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