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Transmission Defects Localization Network: Towards Wrong Assembly in the Transmission Assembly Process

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DOI: 10.23977/acss.2023.070105 | Downloads: 14 | Views: 695

Author(s)

Wenwen Zhu 1, Bin Zhang 2, Tao Song 2, Shuai Zhang 3

Affiliation(s)

1 Chongqing University of Science and Technology, Qinjiagang Street, Chongqing, China
2 Luzhou Rongda Intelligent Transmission Co. LTD, Tai'an Street, Luzhou, China
3 China National Tobacco Corporation Chongqing Company Youyang Branch, Chongqing, China

Corresponding Author

Shuai Zhang

ABSTRACT

Transmission valve component is an important part of automobile manufacture. The success of the assembly of transmission valve is directly related to driving safety of vehicles. While localizing transmission assembly defects is particularly important in assembly of transmission valve component. As an image processing problem, real-time assembly images of transmission valve component are adopted to determine whether the assembly is correct or wrong. Transmission valve component in these images have small, severe reflection, and sparse properties, which increases the difficulty of detection. Therefore, this paper proposes a transmission defects localization network based on Siamese network for improving the performance of assembly of transmission valve components. In our model, we establish an image similarity evaluation network with designed multi-scale features fusion approach. Furthermore, in order to reduce intra-class spacing by similar transmission valve part samples on evaluation action, an improved binary cross entropy and focal loss function is discovered for feature re-processing. Finally, experimental results on real-world transmission assembly dataset indicate that our proposed approach outperforms other compared methods.

KEYWORDS

Transmission assembly, defects localization network, features fusion

CITE THIS PAPER

Wenwen Zhu, Bin Zhang, Tao Song, Shuai Zhang, Transmission Defects Localization Network: Towards Wrong Assembly in the Transmission Assembly Process. Advances in Computer, Signals and Systems (2023) Vol. 7: 37-45. DOI: http://dx.doi.org/10.23977/acss.2023.070105.

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