Abstract
Aiming to settle the problem of knowledge fragmentation, to improve practical and innovation ability of students, research and practice about the innovative experimental course of machine vision project development based on intelligent hardware are carried out. A project-guided case-driven teaching scheme is designed. Taking the project as leading factor, the knowledge involved in the project is decomposed and the knowledge map is made firstly. Then, summarize the relevant knowledge into several modules, and design several experimental cases for each module to promote students to learn the involved knowledge. Finally, through the positive comprehensive project training, students are driven to construct, integrate and internalize knowledge, and their engineering application and innovation ability are effectively improved. The implementation of the course reveals that the course design is reasonable and the project-guided and case-driven teaching scheme works efficiently.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Raut, R., Krit, S., Chatterjee, P.: Machine Vision for Industry 4.0: Applications and Case Studies. CRC Press, Boca Raton (2022)
Beier, M.E., Kim, M.H., Saterbak, A., Leautaud, V., Bishnoi, S., Gilberto, J.M.: The effect of authentic project-based learning on attitudes and career aspirations in STEM. J. Res. Sci. Teach. 56(1), 3–23 (2019)
Kokotsaki, D., Menzies, V., Wiggins, A.: Project-based learning: a review of the literature. Improv. Sch. 19(3), 267–277 (2016)
Bell, S.: Project-based learning for the 21st century: skills for the future. The Clearing House 83(2), 39–43 (2010)
Fernandes, S., Mesquita, D., Flores, M.A., Lima, R.M.: Engaging students in learning: findings from a study of project-led education. Eur. J. Eng. Educ. 39(1), 55–67 (2014)
Karacalli, S., Korur, F.: The effects of project-based learning on students’ academic achievement, attitude, and retention of knowledge: the subject of “electricity in our lives.” Sch. Sci. Math. 114(5), 224–235 (2014)
Mischie, S.: On teaching raspberry Pi for undergraduate university programmes. In: 2016 12th IEEE International Symposium on Electronics and Telecommunications (ISETC), pp. 149–153, Timisoara, Romania (2016)
Ciolacu, M.l., Tehrani, A.F., Svasta, P., Tache, I., Stoichescu, D.: Education 4.0: an adaptive framework with artificial intelligence, raspberry Pi and wearables - innovation for creating value. In: 2020 IEEE 26th International Symposium for Design and Technology in Electronic Packaging (SIITME), pp. 298–303, Pitesti, Romania (2020)
Fan, H., Li, D., Liu, T., Cui, F.: Using interesting examples for teaching digital image processing course. In: 2009 4th International Conference on Computer Science & Education, pp. 1729–1732, Nanning, China (2009)
Zhao, H., Tang, J., Luo, B.: Teaching reform and innovation of the course - digital image processing experiments. In: 2010 5th International Conference on Computer Science & Education, pp. 1599–1600, Hefei, China (2010)
Jiao, L., Zhao, J.: A survey on the new generation of deep learning in image processing. IEEE Access 7, 172231–172263 (2019)
Wang, X., He, H., Li, P., Zhang, L.: Research on the disciplinary evolution of deep learning and the educational revelation. In: 2019 14th International Conference on Computer Science & Education (ICCSE), pp. 655–660, Toronto, ON, Canada (2019)
Wang, Z., Tang, C., Sima, X., Zhang, L.: Research on application of deep learning algorithm in image classification. In: 2021 IEEE Asia-Pacific Conference on Image Processing, Electronics and Computers (IPEC), pp. 1122–1125, Dalian, China (2021)
Acknowledgement
This work is supported by Undergraduate Curricula Construction Program of Harbin Institute of Technology in Shenzhen (No. INEP1023) and Educational Science Planning Project of Shenzhen (No. ybzz19017).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Liu, G., Liu, N., Chen, R., Song, J., Dai, K., Lei, Q. (2023). Research and Practice About Innovative Experimental Course of Machine Vision Project Development. In: Hong, W., Weng, Y. (eds) Computer Science and Education. ICCSE 2022. Communications in Computer and Information Science, vol 1812. Springer, Singapore. https://doi.org/10.1007/978-981-99-2446-2_45
Download citation
DOI: https://doi.org/10.1007/978-981-99-2446-2_45
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-99-2445-5
Online ISBN: 978-981-99-2446-2
eBook Packages: Computer ScienceComputer Science (R0)