Keywords:
automated inspection
defect detection
machine vision
MATLAB
quality control

  • Abstract

    In the cosmetic industry, the quality of perfume bottle packaging was essential for both aesthetic appeal and functionality, as plastic bottles were susceptible to defects that could compromise their attractiveness and usability. Traditionally, these bottles were subjected to manual inspection, a method that was both time-consuming and frequently inaccurate. However, the bottle is manufactured with molding techniques prone to have surface defect that impact structural integrity and appearance of the bottles. Machine vision technology is one of the approaches to improve defect identification with better accuracy. This paper aims to evaluate the designated automated machine vision system to detect defects in plastic perfume bottles with greater efficiency. The system utilized a USB camera and MATLAB software to capture and analyze images of the bottles, employing a black and white detection method to identify imperfections. It integrated a laptop, Arduino UNO, LED light bar, USB camera, MG995 servomotor, and power bank to facilitate the automated sorting of defective bottles. It is determined that threshold 0.2 is the most suitable for the accuracy testing. The automated bottle defect detector manages to obtain an accuracy of more than 95% for every batch inspected. Optimizing the timing of image capture and rotation, incorporating advanced image processing algorithms, and integrating automated systems for bottle placement and removal can significantly improve performance. The overall reliability and accuracy of automatic inspection systems can be further enhanced. This innovative approach provided notable advantages, including more reliable and scalable inspections, which contributed to superior product quality, heightened customer satisfaction, and potential cost savings.

  • References

    1. Azamfirei, V., Psarommatis, F., & Lagrosen, Y. (2023). Application of automation for in-line quality inspection, a zero-defect manufacturing approach. Journal of Manufacturing Systems. 67. 1-22. https://doi.org/10.1016/j.jmsy.2022.12.010
    2. Briga-Sá, A., Ferreira,L., Paulo,B. ,Bentes,I. and Teixeira, C.A. (2023). Energy and environmental performance assessment of reused PET bottles panels for building thermal insulation solutions. Energy Build. 298(4). https://doi.org/10.1016/j.enbuild.2023.113529
    3. Chang,K., C, Chang, H.Wang, Amesimenu.G.D.K, 2021.Machine Vision Welding Defect Detection Based on FPGA. 16th International Microsystems, Packaging, Assembly and Circuits Technology Conference (IMPACT), Taipei, Taiwan. 193-196, https://doi.org/10.1109/IMPACT53160.2021.9696609
    4. Kazmi, M., Hafeez, B., Khan, H. R., & Qazi, S. A. (2022). Machine-Vision-Based Plastic Bottle Inspection for Quality Assurance. Engineering Proceedings, 20(1), 9 https://doi.org/10.3390/engproc2022020009
    5. Li, C., Yu, C., & Lin, H. (2023). DesNet: PCB defect detection network based on deformable convolution. 2023 IEEE 3rd International Conference on Electronic Technology, Communication and Information, ICETCI 2023, 677–681. https://doi.org/10.1109/ICETCI57876.2023.10176845
    6. Luo,Y.M, Nguyen,T.T, Attar,H, Chevalier,L and Lesueur.F, 2022 .A new biaxial apparatus for tensile tests on Poly Ethylene Terephthalate optimized specimen at stretch blow molding conditions. Polym. Test. 113(6). 107676. https://doi.org/ 10.1016/j.polymertesting.2022.107676
    7. Lv, S., Liu, K., Qiao, Y., Li, Y., Sun, Y., & Li, Z. (2020). Automatic Detection Method for Small Size Transmission Lines Defect Based on Improved YOLOv3. Proceedings - 2020 International Conference on Communications, Information System and Computer Engineering, CISCE 2020, 78–81. https://doi.org/10.1109/CISCE50729.2020.00022
    8. Rahman, N.N., Saad, N.M., Abdullah, A.R., & Wahab, F.A. (2018). The Internet of Things Beverages Bottle Shape Defect Detection using Naïve Bayes Classifier. International Journal of Human and Technology Interaction (IJHaTI). 2(1). 71-76
    9. Song, K. K., Zhao, M., Liao, X., Tian, X., Zhu, Y., Xiao, J., & Peng, C. (2022). An Improved Bearing Defect Detection Algorithm Based on Yolo. Proceedings - 2022 International Symposium on Control Engineering and Robotics, ISCER 2022, 184–187. https://doi.org/10.1109/ISCER55570.2022.00038
    10. Uraon, P.K., Verma, A. and Badholia,A. (2022), Steel Sheet Defect Detection using Feature Pyramid Network and RESNET, 2022 International Conference on Edge Computing and Applications (ICECAA),Tamilnadu, India, 1543-1550, https://doi.org/ 10.1109/ICECAA55415.2022.9936318
    11. Wang, S., Yang, S., Xu, P., Ren, M., & Xu, X. (2021). Substation Equipment Defect Detection based on Temporal-spatial Similarity Calculation. Proceedings - 2021 3rd International Conference on Electrical Engineering and Control Technologies, CEECT 2021, 293–297. https://doi.org/10.1109/CEECT53198.2021.9672639
    12. Xin,W., Liu, W. and Yan, K.(2022). Novel Automatic Workpiece Surface Defects Detection System, 22nd International Conference on Control, Automation and Systems (ICCAS). 798-803. doi: 10.23919/ICCAS55662.2022.10003744
Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Copyright (c) 2025 The Authors

How to cite

Ismail, A. N., Rafan, N. A., & Norizan, N. A. (2025). Automated defect detection in perfume bottle packaging using machine vision approach for improved quality control. Multidisciplinary Science Journal, 7(8), 2025400. https://doi.org/10.31893/multiscience.2025400
Crossref
Scopus
Google Scholar
Europe PMC
  • Article viewed - 770
  • PDF downloaded - 510