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.
Index Terms—Course construction, emerging engineering education, engineering application and innovation abilities, project-based learning, machine vision.
The authors are with the School of Information Science and Engineering, East China University of Science and Technology, Shanghai, China (corresponding author: Rong Wang; e-mail: firstname.lastname@example.org, email@example.com, firstname.lastname@example.org, email@example.com; firstname.lastname@example.org).
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.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).