doi: 10.18178/ijiet.2022.12.10.1719
Machine Vision Algorithm Training Course Construction with PBL
Cuiling Jiang
,
Yongjing Wan ,
Yu Zhu ,
Rong Wang*
Abstract
Interdisciplinary integration of theory and practice is imperative as a course requirement in emerging engineering education, and in the public elective course "Machine Vision Algorithm Training". Considering the entire teaching process, including pre-training, in-training, and post-training, this paper discusses the course construction and content in detail in terms of project-based learning (PBL). The PBL teaching approach and evaluation methods are described in detail through a comprehensive face recognition training case based on a convolutional neural network (CNN) and Raspberry Pi. Through project design training from shallower to deeper, interdisciplinary integration of theory and practice is cultivated, stimulating interest in course study. The results demonstrate that PBL teaching improves the engineering application and innovative abilities of students.
Keywords
- Course construction
- emerging engineering education
- engineering application and innovation abilities
- project-based learning
- machine vision
How to Cite
Cuiling Jiang, Yongjing Wan, Yu Zhu, and Rong Wang, "Machine Vision Algorithm Training Course Construction with PBL," International Journal of Information and Education Technology, vol. 12, no. 10, pp. 1050-1055, 2022. https://doi.org/10.18178/ijiet.2022.12.10.1719
Copyright & License
Copyright © 2022 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).