Abstract
At present, many small and medium factories which produce printed circuit board for lithium battery protection need to test products manually to determine whether they meet the production requirements due to the lack of a complete assembly line. The detecting efficiency is low. Combined with the current situation, this paper proposes a desktop-sized printed circuit board automatic detection system, which consists of four parts: camera, manipulator, PC and special tester. In order to improve the detection and location accuracy of detected points on printed circuit board, this paper proposed a detected point recognition algorithm based on YOLOv5 target detection algorithm, a hand-eye calibration algorithm based on neural network fitting and an approximate double-parallel scatter classification algorithm based on dynamic relaxation voting. Experimental results show that the average localisation error of the system is \(0.71 \pm 0.03\) mm and the average image detected time is 2.88 s, which meet the design requirements.
Supported by the science and technology innovation activity plan for college students in Zhejiang Province in 2021 and the new talent plan (2021R407012).
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Liu, Z., Xiong, H., Xiao, S.L., et al.: Positioning of circular mark in PCB based on PCA and segment RHT. J. Chongqing Univ. Technol. (2017)
Wu, Z., Chen, F., Liang, G., et al.: Accurate localization of defective circular PCB mark based on sub-pixel edge detection and least square fitting. In: 2019 IEEE 8th Data Driven Control and Learning Systems Conference (DDCLS), pp. 465–470. IEEE (2019)
Melnyk, R., Hatsosh, D., Levus, Y.: Contacts detection in PCB image by thinning, clustering and flood-filling. In: 2021 IEEE 16th International Conference on Computer Sciences and Information Technologies (CSIT), vol. 1, pp. 370–374. IEEE (2021)
Cho, T.H.: Recognition of characters printed on PCB components using deep neural networks. J. Semicond. Disp. Technol. 20(3), 6–10 (2021)
Lu, P., Liu, Q., Guo, J.: Camera calibration implementation based on Zhang Zhengyou plane method. In: Jia, Y., Du, J., Li, H., Zhang, W. (eds.) Proceedings of the 2015 Chinese Intelligent Systems Conference. LNEE, pp. 29–40. Springer, Heidelberg (2016). https://doi.org/10.1007/978-3-662-48386-2_4
Ali, I., Suominen, O., Gotchev, A., et al.: Methods for simultaneous robot-world-hand-eye calibration: a comparative study. Sensors 19(12), 2837 (2019)
Li, M., Du, Z., Ma, X., et al.: A robot hand-eye calibration method of line laser sensor based on 3D reconstruction. Robot. Comput. -Integr. Manuf. 71, 102136 (2021)
Hua, J., Zeng, L.: Hand-eye calibration algorithm based on an optimized neural network. In: Actuators, vol. 10, no. 4, p. 85.201920. Multidisciplinary Digital Publishing Institute (2021)
Redmon, J., Divvala, S., Girshick, R., et al.: You only look once: unified, real-time object detection. In: Proceedings of the IEEE Conference on Computer Vision And Pattern Recognition, pp. 779–788 (2016)
Lin, Y.L., Chiang, Y.M., Hsu, H.C.: Capacitor detection in PCB using YOLO algorithm. In: 2018 International Conference on System Science and Engineering (ICSSE), pp. 1–4. IEEE (2018)
Dou, Z.H., Liu, X.M., Yin, J.L., et al.: Mounted component inspection technology based on YOLO v3. Electron. Meas. Technol. 44(13), 5 (2021)
Wu, J.G., Cheng, Y., Shao, J., et al.: A defect detection method for PCB based on the improved YOLOv4. Chin. J. Sci. Instrum. 42(10), 8 (2021)
Li, J., Gu, J., Huang, Z., et al.: Application research of improved YOLO V3 algorithm in PCB electronic component detection. Appl. Sci. 9(18), 3750 (2019)
Remy, S., Dhome, M., Lavest, J.M., et al.: Hand-eye calibration. In: Proceedings of the 1997 IEEE/RSJ International Conference on Intelligent Robot and Systems. Innovative Robotics for Real-World Applications. IROS 1997, vol. 2, pp. 1057–1065. IEEE (1997)
Duda, R.O., Hart, P.E.: Use of the Hough transformation to detect lines and curves in pictures. Commun. ACM 15(1), 11–15 (1972)
Lu, C.H., Xu, S.H., Liu, C.H.: Application of digital image process in the detection of printed circuit board. Chin. J. Sci. Instrum. 22, 426–429 (2021)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Xia, H., Pu, C., Wang, B., Liu, Z., Fang, Y. (2022). Desktop-Sized Lithium Battery Protection Printed Circuit Board Detection System Based on Visual Feedback Manipulator. In: Liu, H., et al. Intelligent Robotics and Applications. ICIRA 2022. Lecture Notes in Computer Science(), vol 13458. Springer, Cham. https://doi.org/10.1007/978-3-031-13841-6_31
Download citation
DOI: https://doi.org/10.1007/978-3-031-13841-6_31
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-13840-9
Online ISBN: 978-3-031-13841-6
eBook Packages: Computer ScienceComputer Science (R0)