Edge Impulse Potential to Enhance Object Recognition Through Machine Learning

Gabriele Regina Pinaso; Leonardo Marcondes Figueiredo; Orlando Rosa Júnior; Marco Rogério  da Silva Richetto

doi:10.5433/1679-0375.2024.v45.49197

Edge Impulse Potential to Enhance Object Recognition Through Machine Learning

Authors

Gabriele Regina Pinaso SENAI Félix Guisard College of Technology https://orcid.org/0009-0001-7360-1179
Leonardo Marcondes Figueiredo SENAI Félix Guisard College of Technology https://orcid.org/0009-0008-6295-8472
Orlando Rosa Júnior SENAI Félix Guisard College of Technology https://orcid.org/0000-0002-3894-7879
Marco Rogério da Silva Richetto SENAI Félix Guisard College of Technology https://orcid.org/0000-0003-3755-4341

DOI:

https://doi.org/10.5433/1679-0375.2024.v45.49197

Keywords:

artificial intelligence, machine learning, object recognition, low-code applications

Abstract

Machine Learning (ML) is a powerful artificial intelligence branch that can help businesses, whether small or large, in a variety of industries. It is an option for replacing resources with high operating costs. The aim of this study was to use the Edge Impulse platform as an ML tool option. The system applies low-code frameworks which abstracts a series of complex techniques applied in ML, such as data processing and AI components structure. It implies a time reduction during the development period. Using Edge Impulse allows a more user-friendly interface alternative with an easy-to-interpret logic flow. The study focused an application to do an object recognition, aiming the system capacity limit. The autogenerated accuracy value, pointed by the system indicated 97.9 % after the training step and 89 % after retesting the first 20 mices, photographed in different image angles, indicating a possible model overfitting. Even though, the system showed promise in terms of classifying objects. Some adjustments in the image dataset can improve the model capacity of recognition, as the amount of images showed insufficient at the survey's conclusion.

Author Biographies

Gabriele Regina Pinaso, SENAI Félix Guisard College of Technology

Postgraduate Student, SENAI Félix Guisard College of Technology, Taubaté, São Paulo, Brazil

Leonardo Marcondes Figueiredo, SENAI Félix Guisard College of Technology

Postgraduate Student, SENAI Félix Guisard College of Technology, Taubaté, São Paulo, Brazil

Orlando Rosa Júnior , SENAI Félix Guisard College of Technology

MSc Professor, SENAI Félix Guisard College of Technology, Taubaté, São Paulo, Brazil

Marco Rogério da Silva Richetto, SENAI Félix Guisard College of Technology

PhD student, Prof., SENAI College of Technology, Félix Guisard, Taubaté, SP, Brazil.

References

Abdullah, A., & Yih, T. Y. (2014). Implementing Learning Contracts in a Computer Science Course as a Tool to Develop and Sustain Student Motivation to Learn. Procedia - Social and Behavioral Sciences, 123, 256–265.

Bilik, S., & Horak, K. (2022). SIFT and SURF based feature extraction for the anomaly detection. ArXiv, 2, 1-7.

Block, S. B., da Silva, R. D., Dorini, L. B., & Minetto, R. (2021). Inspection of Imprint Defects in Stamped Metal Surfaces Using Deep Learning and Tracking. IEEE Transactions on Industrial Electronics, 68(5), 4498–4507.

Calixto, J. M. T., Corrêa, M. S., & De Oliveira, M. A. (2022). A empregabilidade da inteligência artificial na automação do setor logístico para controle de carga. Episteme Transversalis, 13(2), 1–30.

Carou, D., Sartal, A., & Davim, J. P. (2022). Machine Learning and Artificial Intelligence with Industrial Applications: From Big Data to Small Data. Springer.

Cioffi, R., Travaglioni, M., Piscitelli, G., Petrillo, A., & De Felice, F. (2020). Artificial intelligence and machine learning applications in smart production: Progress, trends, and directions. Sustainability, 12(2), 1–26.

Cınar, Z. M., Nuhu, A. A., Zeeshan, Q., Korhan, O., Asmael, M., & Safaei, B. (2020). Machine learning in predictive maintenance towards sustainable smart manufacturing in industry 4.0. Sustainability, 12(19), 1–42.

Dalzochio, J., Kunst, R., Pignaton, E., Binotto, A., Sanyal, S., Favilla, J., & Barbosa, J. (2020). Machine learning and reasoning for predictive maintenance in industry 4.0: Current status and challenges. Computers in Industry, 123, 1–15.

Hymel, S., Banbury, C., Situnayake, D., Elium, A., Ward, C., Kelcey, M., Baaijens, M., Majchrzycki, M., Plunkett, J., Tischler, D., Grande, A., Moreau, L., Maslov, D., Beavis, A., Jongboom, J., & Reddi, V. J. (2023). Edge impulse: An mlops platform for tiny machine learning. ArXiv, 3, 1-15.

Jain, V., Wadhwani, K., & Eastman, J. K. (2023). Artificial intelligence consumer behavior: A hybrid review and research agenda. Journal of Consumer Behavior, 23(2), 676–697.

Klein, P., & Bergmann, R. (2018). Data generation with a physical model to support machine learning research for predictive maintenance.

LoPiano, S. (2020). Ethical principles in machine learning and artificial intelligence: Cases from the field and possible ways forward. Humanit Soc Sci Commun, 7(9), 1–7.

Mahesh, B. (2020). Machine learning algorithms a review. International Journal of Science and Research (IJSR), 9(1), 381–386.

Mery, D. (2020). Aluminum casting inspection using deep learning: A method based on convolutional neural networks. Journal of Nondestructive Evaluation, 39, 1–12.

Mezavila, S. A., Dias, A. A., & Franco, M. E. (2021). Aprendizagem de máquina aplicada a análise de batimentos cardíacos. Eixos Tech, 8(1), 1–14.

Mihigo, I. N., Zennaro, M., Uwitonze, A., Rwigema, J., & Rovai, M. (2022). On-device iot-based predictive maintenance analytics model: Comparing tinylstm and tinymodel from edge impulse. Sensors, 22(14), 1–20.

Pournader, M., Ghaderi, H., Hassanzadegan, A., & Fahimnia, B. (2021). Artificial intelligence applications in supply chain management. International Journal of Production Economics, 241, 1–16.

Raschka, S., Patterson, J., & Nolet, C. (2020). Machine learning in python: Main developments and technology trends in data science, machine learning, and artificial intelligence. Information, 11(4), 1–44.

Sarker, I. H. (2021). Machine learning: Algorithms, real-world applications and research directions. SN Computer Science, 2, 1–21.

Schulz, A., Stathatos, S., Shriver, C., & Moore, R. (2023). Utilizing online and open-source machine learning toolkits to leverage the future of sustainable engineering. ArXiv, 1, 1–13.

Sundaram, S., & Zeid, A. (2023). Artificial intelligence based smart quality inspection for manufacturing. Micromachines, 14(3), 1–19.

Tarantino, A. (2022). Smart Manufacturing: The lean six sigma way. John Wiley & Sons.

Taulli, T. (2020). Introdução à Inteligência Artificial: uma Abordagem Não Técnica. Novatec Editora.

Winfield, A. F. T., & Jirotka, M. (2018). Ethical governance is essential to building trust in robotics and artificial intelligence systems. Philosophical Transactions of the Royal Society A, 376(2133), 1–13.

Xuecai, X., Gui, F., Yujingyang, X., Zigi, Z., Ping, C., Baojun, L., & Song, J. (2019). Risk prediction and factors risk analysis based on ifoa-grnn and apriori algorithms: Application of artificial intelligence in accident prevention. Process Safety and Environmental Protection, 122, 169–184.