Development of a Sequential Neural Network Model for Bottle-Fill Level Detection and Classification
AbstractMachine vision is one of the cutting-edge technologies that can assist human operators in tasks such as bottle-fill level detection and classification, resulting in increased efficiency in the bottling industry. Although pre-trained models such as the MobileNet, ResNet-50, and VGG-19 for bottle-fill level detection and classification exist, their accuracy is dependent on the similarity of their trained data to the application domain. As a result, this paper describes how to create a sequential neural network model for bottle-fill level detection and classification in Python 3.8.3. The proposed model is evaluated and compared to the MobileNet, ResNet-50, and VGG-19 models in a multiclass problem (correctly filled, overfilled, and underfilled). Furthermore, a confusion matrix was used to assess the performance of the proposed model in the correctly filled, overfilled, and underfilled categories of filled bottles. In comparison to the MobileNet, ResNet50, and VGG-19 models, the proposed model had a training and testing dataset accuracy of 98%, while the MobileNet had 73%, ResNet50 had 76%, and VGG-19 had 75%. The accuracy of the confusion matrix on 40 sample sizes for each class of the filled level was 97%. Finally, in the application domain, the proposed model outperforms the MobileNet, ResNet50, and VGG-19 models. As a result, the method used in the neural network layer structuring of the sequential neural network model should be considered a viable alternative in similar applications.
Akundi, A., & Reyna, M. (2021). A Machine Vision Based Automated Quality Control System for Product Dimensional Analysis. Procedia Computer Science, 185, 127-134. https://doi.org/10.1016/j.procs.2021.05.014
Anush, C., Yashwanth, K., Shashank, S., Venkat, R., & Ashwani, K. (2021). Bottle Line Detection using Digital Image Processing with Machine Learning. Journal of Physics: Conference Series, 1998, 1-6. 10.1088/1742-6596/1998/1/012033
Bahaghigat, M., Abedini, F., S'hoyan, M., & Molnar, A.-J. (2019). Vision Inspection of Bottle Caps in Drink Factories Using Convolutional Neural Networks. IEEE 15th International Conference on Intelligent Computer Communication and Processing, Romania, 381-385. doi: 10.1109/ICCP48234.2019.8959737
Ismail, N., & Malik, O. A. (2021). Real-time visual inspection system for grading fruits using computer vision and deep learning techniques. Information Processing in Agriculture, 9(1), 24-37. https://doi.org/10.1016/j.inpa.2021.01.005
Koodtalang, W., Sangsuwan, T., & Sukanna, S. (2019). Glass Bottle Bottom Inspection Based on Image Processing and Deep Learning. Research, Invention, and Innovation Congress. Thailand, 1-5. doi: 10.1109/RI2C48728.2019.8999883
Kumar, P., & Ramakrishna, H. V. (2015). Automated Bottle Cap Inspection Using MachineVision System. International Journal of Innovative Research In Technology, 131-136.
McDermott, J. (2022, December 16). LearnDataSci. Retrieved from Learndatasci: https://www.learndatasci.com/tutorials/hands-on-transfer-learning-keras
Nazim , K., & Sattar, A. (2020). Automated Water Tap Controlling System Using Machine Vision. IJCSNS International Journal of Computer Science and Network Security, 19(12), 91-95. DOI: 10.13140/RG.2.2.26680.08961.
Opeyemi, A & Oyeyemi T. O. (2023). Development of a Sign Language E-Tutor Using Convolutional Neural Network. FUOYE Journal of Engineering and Technology, 8(2), 192-196. http://doi.org/10.46792/fuoyejet.v8i2.1055
Parakontan, T., & Sawangsri, W. (2019). Development of the Machine Vision System for Automated Inspection of Printed Circuit Board Assembly. 3rd International Conference on Robotics and Automation Sciences, China, 244-248. doi: 10.1109/ICRAS.2019.8808980.
Rong, D., Wanga, H., Xie, L., Ying, Y., & Zhanga, Y. (2020). Impurity detection of juglans using deep learning and machine vision. Computers and Electronics in Agriculture, 178, 1-9. https://doi.org/10.1016/j.compag.2020.105764
Tao, X., Wang, Z., Zhang, Z., Zhang, D., Xu, D., Gong, X., & Zhang, L. (2018). Wire Defect Recognition of Spring-Wire Socket Using Multitask Convolutional Neural Networks. in IEEE Transactions on Components, Packaging and Manufacturing Technology, 8(4), 689-698. doi: 10.1109/TCPMT.2018.2794540..
Wang, J., Fua, P., & Gao, R. (2019). Machine vision intelligence for product defect inspection based on deep learning and Hough transform. Journal of Manufacturing Systems, 51, 52-60. https://doi.org/10.1016/j.jmsy.2019.03.00
Zhou , X., Wang, Y., Xiao, C., Zhu, Q., Lu, X., Zhang, H., . . . Zhao, H. (2019). Automated Visual Inspection of Glass Bottle Bottom With Saliency Detection and Template Matching. in IEEE Transactions on Instrumentation and Measurement, 68(11), 4253-4267, doi: 10.1109/TIM.2018.288697
Copyright (c) 2023 The Author(s)
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
The authors hereby represent and warrant that the paper is original and that they are the authors of the paper, except for material that is clearly identified as to its original source, with permission notices from the copyright owners where required. If in future any violation of any copyright come in notice, then the author will be responsible and not FUOYEJET.
The authors declare that:
- This paper has not been published in the same form elsewhere.
- It will not be submitted anywhere else for publication prior to acceptance/rejection by this Journal.
- A copyright permission is obtained for materials published elsewhere and which require this permission for reproduction.
Furthermore, the copyright after publication belongs to the Author(s) (for articles published in 2020 and beyond) and licensed under the creative commons license CC-BY-NC (http://creativecommons.org/licenses/by-nc/4.0). The copyright covers the right to reproduce and distribute the article, including reprints, translations, photographic reproductions, microform, electronic form (offline, online) or any other reproductions of similar nature.