ARTICLE
Volume 8,Issue 7
29 May 2025
Optimizing Computer Vision Pedagogy: An Industry-Education Integration Approach
Yi Ren ,
Lei Li
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1
Suzhou Polytechnic Institute of Agriculture, Suzhou 215008, Jiangsu, China,
2
Suzhou DCCK Technology Co. Ltd., Suzhou 215008, Jiangsu, China,
erd 2025 , 7(5), 20–26;
https://doi.org/10.26689/erd.v7i5.10572
© 2025 by the Authors. Licensee Whioce Publishing, Singapore. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution 4.0 International License ( https://creativecommons.org/licenses/by/4.0/ )
Abstract
In the context of Industry 4.0, the use of machine vision and image technology is becoming increasingly widespread. This trend not only drives advancements across industries but also raises the bar for cultivating skilled professionals. The industry-education integration has gradually emerged as a crucial approach for vocational colleges to align with societal development. This model effectively bridges talent development with industry needs, addressing the gap between the skills taught in schools and those required by enterprises. This paper emphasizes the significance of optimizing computer vision course instruction in vocational colleges through the lens of industry-education integration. It examines the challenges in current teaching practices and proposes targeted reform strategies aimed at enhancing the quality of talent cultivation. By doing so, it seeks to produce more machine vision specialists to meet societal demands while fostering deeper collaboration between industry and academia.
References
Zhang X, 2025, Research on the Docking Mechanism Between Computer Courses and Industry in Higher Vocational Colleges From the Perspective of Integration of Industry and Education. Modern Vocational Education, 2025(4): 121–124.
Long K, 2025, An Analysis on the Teaching Reform Practice of Basic Computer Courses in Colleges and Universities Under the Background of Integration of Production and Education. Information Systems Engineering, 2025(1): 165–168.
Zhao J, Zhu Y, Feng X, et al., 2025, Reform of Teaching Models for University Computer Courses Under the Background of Deep Integration of Industry and Education. Computer Education, 2025(1): 50–54.
Li X, Luo H, 2024, Research on the Reform and Practice of Machine Vision Course Teaching Under the Background of Industry–Education Integration. Computer Knowledge and Technology, 20(36): 152–153 + 160.
Zhang K, 2025, Research on Training Problems and Countermeasures of Computer Professional in Secondary Vocational Schools Under the Background of Integration of Industry and Education. Journal of Science, 2025(2): 16–18.
Yang C, Peng H, Wang L, 2024, Reform and Innovation of Online and Offline Mixed Teaching Mode of “Computer Vision Application Development” Course. Journal of Anhui Electronic Information Vocational and Technical College, 23(4): 31–34.
Tang Z, Zhang Z, Zhu K, et al., 2019, Research on Teaching Reform of “Image Processing and Machine Vision” Course in Local Applied Universities Under the Background of New Engineering. Journal of Liupanshui Normal University, 36(6): 106–120.
Zhong X, 2024, Research on Training Model of Computer Majors in Higher Vocational Colleges Under the Concept of Integration of Production and Education. Science and Education Guide, 2024(33): 71–73.
Gong S, Zhang C, Liu H, et al., 2024, Research on Optimal Practice Path of Compound Talent Cultivation System Based on Integration of Industry and Education and School–Enterprise Cooperation Under the Background of Engineering Certification. Modern Vocational Education, 2024(33): 41–44.
Su N, 2019, Analysis on Talent Training Strategy of Integration of Industry and Education in Computer Application Technology Major Under the Background of “Three Education” Reform. Paper Making Equipment and Materials, 53(11): 215–217.
Xie X, Baocai J, Wang H, et al., 2024, Exploration of Computer Talents Training Model in Local Universities Based on Integration of Industry and Education. Sichuan Labor Security, 2024(10): 125–126.
Xu X, Liu Y, Sui T, et al., 2024, Research on Teaching Reform of Machine Vision and Image Understanding Based on Talent Training Needs. Journal of Science, 2024(31): 5–8.
Zhang Z, He L, 2023, Exploration of Teaching Reform for “Machine Vision” Course in Vocational Teacher Education Institutions. Science and Technology Wind, 2023(19): 111–113.
Zhang L, Shen Y, 2023, Design and Practice of Integrated Teaching Case of Computer Vision Course. Journal of Higher Education, 9(16): 47–50.
Zhong M, 2022, Curriculum Reform of Undergraduate Computer Vision Based on New Engineering. China New Communications, 24(21): 134–136.
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