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).