Publication Type
Conference Proceeding Article
Version
acceptedVersion
Publication Date
10-2024
Abstract
Industry 4.0, the digitalization of manufacturing promises to lead to lowered cost, efficient processes and even discovery of new business models. However, many of the enterprises have huge investments in legacy machines which are not 'smart'. In this study, we thus designed a cost-efficient solution to retrofit a legacy conveyor belt-based cutlery washing machine with a commodity web camera. We then applied computer vision (using both traditional image processing and deep learning techniques) to infer the speed and utilization of the machine. We detailed the algorithms that we designed for computing both speed andutilization. With the existing operational constraints of our client, frequent re-training of the deep learning model for object detection is not feasible. Thus, we compared the generalizability of the two techniques across 'unseen' cutleries and found traditional image processing to be generalizable across 'unseen' images. Our proposed final solution uses traditional image processing for computation of utilization but a hybrid of traditional image processing and deep learning model for speed computation as it is more reliable. Our client has implemented our proposed solution for one conveyor belt-based cutlery washing machine and will be planning to scale this to multiple conveyor belt-based cutlery washing machines.
Keywords
Industry 4.0, Computer Vision, Deep Learning, Image Processing.
Discipline
Artificial Intelligence and Robotics | Databases and Information Systems
Research Areas
Data Science and Engineering
Publication
Proceedings of the 16th International Conference, ICCCI 2024, Leipzig, Germany, September 9-11
First Page
301
Last Page
313
ISBN
9783031702587
Identifier
10.1007/978-3-031-70259-4
Publisher
Springer
City or Country
Cham
Citation
FWA, Hua Leong.
Retrofitting a legacy cutlery washing machine using computer vision. (2024). Proceedings of the 16th International Conference, ICCCI 2024, Leipzig, Germany, September 9-11. 301-313.
Available at: https://ink.library.smu.edu.sg/sis_research/9302
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1007/978-3-031-70259-4