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Academic Journal of Engineering and Technology Science, 2021, 4(8); doi: 10.25236/AJETS.2021.040805.

Detection System for Mobile Phone Interface Circuit Board Assembly Based on Computer Vision


Hongyu Chen

Corresponding Author:
Hongyu Chen

College of Mechanical and Electronic Engineering, Northwest A&F University, Yangling, Shaanxi, 712100, China


The quality of Mobile Phone Interface Circuit Board Assembly (MPICBA), as one of the main components of mobile, has a direct impact on the entire performance and service life of the final mobile phone, therefore it is highly regarded and widely worried in the process of mobile phone assembly. At the moment, MPICBA is usually assembled on a semi-automatic assembly line, with low assembly quality and low assembly reliability, relying primarily on manual vision for assembly, which has labor intensity and labor inefficiency issues, and thus cannot meet the stability and reliability requirements of mass production. This paper, titled "Detection System for Mobile Phone Interface Circuit Board Assembly Based on Computer Vision," analyzes the above-mentioned content by studying key technologies for MPICBA, such as image acquisition and processing, image extraction algorithm optimization, and edge algorithm recognition, based on the aforementioned problems. This is used to build and create an detection system for MPICBA using computer vision. This paper analyzes and designs a MPICBA detection system, and proposes a computer vision approach to the detection of MPICBA to address the current problem of low manual efficiency in circuit board assembly, and to achieve online detection and assembly of MPICBA, as well as in the design of the automated system for system stability and repeatability.


Computer Vision; Detection System; Board Assembly; Mobile Phone Interface Circuit

Cite This Paper

Hongyu Chen. Detection System for Mobile Phone Interface Circuit Board Assembly Based on Computer Vision. Academic Journal of Engineering and Technology Science (2021) Vol. 4, Issue 8: 33-42. https://doi.org/10.25236/AJETS.2021.040805.


[1] G. Mahalingam, K. M. Gay and K. Ricanek. PCB-METAL: A PCB Image Dataset for Advanced Computer Vision Machine Learning Component Analysis. International Conference on Machine Vision Applications (MVA). Tokyo, Japan, 16th 2019, p. 1-5.

[2] F. Ardhy and F. I. Hariadi. Development of SBC based machine-vision system for PCB board assembly Automatic Optical detection. International Symposium on Electronics and Smart Devices (ISESD). Bandung, Indonesia, 2016, p. 386-393.

[3] Ji-joong Hong, Kyung-ja Park and Kyung-gu Kim. Parallel processing machine vision system for bare PCB detection. IECON '98. Proceedings of the 24th Annual Conference of the IEEE Industrial Electronics Society (Cat. No.98CH36200). Aachen, Germany, 1998, pp. 1346-1350 vol.3.

[4] J. A. B. Susa, E. Mariquina, M. L. Tria, C. M. Adolfo and J. C. Castro. Cap-Eye-citor: A Machine Vision Inference Approach of Capacitor Detection for PCB Automatic Optical detection. 2020 IEEE 7th International Conference on Engineering Technologies and Applied Sciences (ICETAS). Kuala Lumpur, Malaysia, 2020, p. 1-5.

[5] F. Guo and S. Guan. Research of the Machine Vision Based PCB Defect detection System. 2011 International Conference on Intelligence Science and Information Engineering. Wuhan, China, 2011, p. 472-475.

[6] Jin Huazhong, Ye Zhiwei. Machine Vision Experimental Platform Construction and Teaching Practice. Computer Education. Vol. 11 (2021), p. 62-66.

[7] Wang Weiguo, Zhang Ziming, Liu Liangyong. Research on Aerospace Instrumentation Testing Methods based on Machine Vision. China Plant Engineering. Vol. 21 (2021), p. 155-156.

[8] Hao Shuxin, Lin Jinzhou, Liu Fang. Research on the Application of Machine Vision in the Field of Automobile Quality Inspection. Auto Time. Vol. 21 (2021), p. 16-17.