Teaching Reform and Practice of the Computer Vision Course in Universities under the Background of Emerging Engineering
The in‑depth implementation of emerging‑engineering construction has put forward new requirements for professionalism, practicality, and innovation in engineering course teaching at universities. Computer Vision is a core course for majors such as artificial intelligence, computer science, and software engineering, and it is also one of the core technologies most widely applied in the implementation of artificial intelligence industries. On this basis, this paper briefly analyzes the importance and current teaching status of the Computer Vision course teaching reform in universities under the background of emerging engineering, and explores innovative practical strategies for its teaching reform, so as to provide references for high‑quality teaching of the Computer Vision course and the cultivation of emerging engineering talents in universities.
[1] Dong G, 2025, Promoting Depth Through Depth: Construction of Three‑Dimensional Teaching System for Computer Vision Course. Journal of Science and Education, (32): 14–16.
[2] Sun H, 2025, Research on Teaching Design for Computer Vision Course Based on Action‑Oriented Teaching Mode. Office Automation, 30(18): 32–34.
[3] Zhang N, Ai J, 2025, Blended‑Teaching Design and Practice — Taking the Course Computer Vision Technology as an Example. Scientific Consult, (17): 164–168.
[4] Wang T, Ouyang J, Zhao Y, et al., 2025, Research on Teaching Reform of Computer‑Vision‑Related Courses. China Electric Power Education, (8): 70–71.
[5] Yan Y, 2025, Exploration on Teaching Reform of the Computer Vision Course for Undergraduate Computer‑Science Students. China Information Technology Education, (9): 102–105.
[6] Zhou Y, Zheng Z, Chen Y, et al., 2025, Teaching Reform of the Core Computer Vision Course for Artificial‑Intelligence Undergraduates. Computer Education, (5): 161–165.
[7] Dong Z, Lin Y, Cong R, 2025, Optimization of Emerging‑Engineering Courses and Reconstruction of Evaluation System From the Perspective of System Theory — Taking Computer Vision Course as an Example. Computer Education, (6): 168–172 + 177.
[8] Fu Q, Liu J, Qin S, et al., 2025, Innovative Teaching Design for the Course Computer Vision and Pattern Recognition Based on OBE Concept. Technology Wind, (18): 116–119.
[9] Cai H, 2024, Construction of Undergraduate Computer Vision Course Based on “OBE Concept” and “Multi‑Driven Mode.” University Education, (5): 40–43.
[10] Zhao L, Sun Y, Li J, et al., 2024, Teaching Design and Practice for Cultivating Innovative Artificial‑Intelligence Talents in Emerging‑Engineering Context — Taking Computer Vision Course as an Example. Journal of Shenyang University (Social‑Science Edition), 26(3): 74–82.
[11] Xie Y, Zhen X, Liu J, 2023, Teaching Exploration of the Computer Vision Course Based on Research‑Oriented Mode. Western Quality Education, 9(22): 144–148.
[12] Zhang L, Shen Y, 2023, Design and Practice of Comprehensive Teaching Cases for Computer Vision Course. Journal of Higher Education, 9(16): 47–50.
[13] Liu H, Yu Y, Wang W, 2023, Exploration of PBL Teaching Mode Based on Thinking Visualization — Taking Computer Vision Course as an Example. Computer Education, (5): 76–80.
[14] Zhong M, 2022, Undergraduate Computer Vision Course Reform Under Emerging Engineering. China New Telecommunications, 24(21): 134–136.
[15] Chen Z, Deng Y, Li Z, 2022, Teaching Reform of Computer Vision Course Under the Background of Deep Learning. Journal of Anshun University, 24(4): 122–128.
