Education, Science, Technology, Innovation and Life
Open Access
Sign In
  1. Home
  2. Journals
  3. Advances in Computer, Signals and Systems
  4. Vol 7, Issue 1, 2023

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

Download as PDF

DOI: 10.23977/acss.2023.070105 | Downloads: 14 | Views: 640

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.

REFERENCES

[1] X. Liu, D. Xiang, C. Zeng, and F. Huang, "The investigation of coupled vibration of automobile transmission system and its intelligent design," Machines, vol. 10, no. 6, p. 428, 2022.
[2] B. Gao, D. Meng, W. Shi, W. Cai, S. Dong, Y. Zhang, and H. Chen, "Topology optimization and the evolution trends of two-speed transmission of evs," Renewable and Sustainable Energy Reviews, vol. 161, p. 112390, 2022.
[3] H. Zhu, "Application of arma prediction algorithm in gear fault diagnosis of automobile drive axle," in International Conference on Frontier Computing. Springer, 2022, pp. 1369–1374.
[4] C. Kong, K. Ma, and J. Wei, "Information encryption transmission method of automobile communication network based on neural network," in International Conference on Machine Learning for Cyber Security. Springer, 2023, pp. 243–256.
[5] R. Bogue, "The changing face of the automotive robotics industry," Industrial Robot: the international journal of robotics research and application, 2022.
[6] Z. Ren, F. Fang, N. Yan, and Y. Wu, "State of the art in defect detection based on machine vision," International Journal of Precision Engineering and Manufacturing-Green Technology, vol. 9, no. 2, pp. 661–691, 2022.
[7] M. S. Almutairi, "Deep learning-based solutions for 5g network and 5g-enabled internet of vehicles: advances, meta-data analysis, and future direction," Mathematical Problems in Engineering, vol. 2022, 2022.
[8] Z. Wang, Y. Ling, X. Wang, D. Meng, L. Nie, G. An, and X. Wang, "An improved faster r-cnn model for multi-object tomato maturity detection in complex scenarios," Ecological Informatics, vol. 72, p. 101886, 2022.
[9] Z. Cai and N. Vasconcelos, "Cascade r-cnn: Delving into high quality object detection," in Proceedings of the IEEE conference on computer vision and pattern recognition, 2018, pp. 6154–6162.
[10] P. Malge and R. Nadaf, "Pcb defect detection, classification and localization using mathematical morphology and image processing tools," International journal of computer applications, vol. 87, no. 9, 2014.
[11] V. H. Gaidhane, Y. V. Hote, and V. Singh, "An efficient similarity measure approach for pcb surface defect detection," Pattern Analysis and Applications, vol. 21, no. 1, pp. 277–289, 2018.
[12] B. Kaur, G. Kaur, and A. Kaur, "Detection and classification of printed circuit board defects using image subtraction method," in 2014 Recent Advances in Engineering and Computational Sciences (RAECS). IEEE, 2014, pp. 1–5.
[13] T. Liu, L. Huang, and B. Chen, "Real-time defect detection of laser additive manufacturing based on support vector machine," in Journal of Physics: Conference Series, vol. 1213, no. 5. IOP Publishing, 2019, p. 052043.
[14] K. Liu, N. Luo, A. Li, Y. Tian, H. Sajid, and H. Chen, "A new self-reference image decomposition algorithm for strip steel surface defect detection," IEEE Transactions on Instrumentation and Measurement, vol. 69, no. 7, pp. 4732–4741, 2019.
[15] R. Zhang, E. Maggard, Y. Bang, M. Cho, M. Shaw, and J. Allebach, "Streak detection in color printed customer content," Electronic Imaging, vol. 2021, no. 16, pp. 254–1, 2021.
[16] R. Medico, N. Lambrecht, H. Pues, D. V. Ginste, D. Deschrijver, T. Dhaene, and D. Spina, "Machine learning based error detection in transient susceptibility tests," IEEE Transactions on Electromagnetic Compatibility, vol. 61, no. 2, pp. 352–360, 2018.
[17] M. Mahdavi and Z. Abedjan, "Reds: Estimating the performance of error detection strategies based on dirtiness profiles," in Proceedings of the 31st International Conference on Scientific and Statistical Database Management, 2019, pp. 193–196.
[18] L. Yao and J. Miller, “Tiny imagenet classification with convolutional neural networks,” CS 231N, vol. 2, no. 5, p. 8, 2015.
[19] T.-Y. Lin, M. Maire, S. Belongie, J. Hays, P. Perona, D. Ramanan, P. Dollar, and C. L. Zitnick, "Microsoft coco: Common objects in' context," in European conference on computer vision. Springer, 2014, pp. 740–755.
[20] M. Everingham, S. A. Eslami, L. Van Gool, C. K. Williams, J. Winn, and A. Zisserman, "The pascal visual object classes challenge: A retrospective," International journal of computer vision, vol. 111, no. 1, pp. 98–136, 2015.
[21] C.-Y. Chuang, J. Robinson, Y.-C. Lin, A. Torralba, and S. Jegelka, "Debiased contrastive learning," Advances in neural information processing systems, vol. 33, pp. 8765–8775, 2020.
[22] M. Kaya and H. S¸. Bilge, "Deep metric learning: A survey," Symmetry, vol. 11, no. 9, p. 1066, 2019.
[23] I. Melekhov, J. Kannala, and E. Rahtu, "Siamese network features for image matching," in 2016 23rd international conference on pattern recognition (ICPR). IEEE, 2016, pp. 378–383.
[24] D. Chicco, "Siamese neural networks: An overview," Artificial Neural Networks, pp. 73–94, 2021.
[25] W. Xiao and Y. Ding, "A two-stage siamese network model for offline handwritten signature verification," Symmetry, vol. 14, no. 6, p. 1216, 2022.
[26] Y. Zhang, L. Wang, J. Qi, D. Wang, M. Feng, and H. Lu, "Structured siamese network for real-time visual tracking," in Proceedings of the European conference on computer vision (ECCV), 2018, pp. 351–366.
[27] J. Li, H. He, H. He, L. Li, and Y. Xiang, "An end-to-end framework with multisource monitoring data for bridge health anomaly identification," IEEE Transactions on Instrumentation and Measurement, vol. 70, pp. 1–9, 2020.
[28] S. Sheynin, O. Ashual, A. Polyak, U. Singer, O. Gafni, E. Nachmani, and Y. Taigman, "Knn-diffusion: Image generation via large-scale retrieval," arXiv preprint arXiv:2204.02849, 2022.
[29] H. Zhang, Y. Wang, F. Dayoub, and N. Sunderhauf, "Varifocalnet: An iou-aware dense object detector," in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2021, pp. 8514–8523.
[30] X. Zhu, W. Su, L. Lu, B. Li, X. Wang, and J. Dai, "Deformable detr: Deformable transformers for end-to-end object detection," arXiv preprint arXiv:2010.04159, 2020.

Subscription
E-Mail Alert
Most popular papers
Call for Special Issue
Publication Ethics & OA Statement
Plagiarism Policy
Contact Us
Downloads: 13157
Visits: 255999

Sponsors, Associates, and Links

All published work is licensed under a Creative Commons Attribution 4.0 International License.

Copyright © 2016 - 2031 Clausius Scientific Press Inc. All Rights Reserved.