Exploring the Construction of the Course 'Image Processing and Computer Vision' for New Engineering Civil Engineering Majors from an Interdisciplinary Integration Perspective

Jiaqi Li; Huaguo Gao; Aiping Fei; Yuze Tian; Bin Yang; Nan Wang

doi:10.6918/IJOSSER.202409_7(9).0010

Authors

Jiaqi Li
Huaguo Gao
Aiping Fei
Yuze Tian
Bin Yang
Nan Wang

DOI:

https://doi.org/10.6918/IJOSSER.202409_7(9).0010

Keywords:

Interdisciplinary Integration; Intelligent Construction; Image Proessing; Computer Vision.

Abstract

This paper discusses the interdisciplinary integration construction of the course "Image Processing and Computer Vision" in the context of intelligent construction to adapt to the intelligent transformation of civil engineering majors in colleges and universities. This paper analyzes the status quo of the course system, emphasizes the importance of interdisciplinary education to cultivate composite talents, and puts forward the principles of course design, learning objectives and evaluation system. Detailed planning of theoretical foundation, technology application, scenario practice and knowledge integration modules, innovative teaching methods, such as project-driven, case study, virtual experiment and school-enterprise cooperation, are designed to enhance students' practical and innovative ability. Finally, the paper summarizes the conclusions and recommendations of the curriculum reform, emphasizing deepening interdisciplinary education, strengthening practical teaching, promoting school-enterprise cooperation and focusing on lifelong learning, with a view to cultivating professionals who will lead the future development of the construction industry.

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References

L Ding: System Thingking on Cultication Mode for Innovative Intelligent Construction Talents, Research in Higher Education of Engineering, Vol. 2019 (2019) No.5, p.1-4+29.

Z Liu, S Liu, Y Z, et al: DEVELOPMENT STATUS AND FUTURE TRENDS OF INTELLIGENT CONSTRUCTION TECHNOLOGY, Architecture Technology, Vol. 50 (2019) No.7, p.772-779.

W Yan, S Wang: Application of unmanned aerial vehicle tilt photography aerial survey technology combined with BIM in smart site system, Journal of Electronic Measurement and Instrumentation, Vol. 33 (2019) No.10, p.59-65.

Y Liu, H Chen, F Liu: Research and application of intelligent perception system for unmanned aerial vehicle inspection at construction site, Power System Protection and Control, Vol. 46 (2018) No.15, p.155-161.

J. Li, X. Zhao, G. Zhou, et al.: Evaluating the Work Productivity of Assembling Reinforcement through the Objects Detected by Deep Learning, Sensors, Vol. 21 (2021), p.5598.

J. Li, G. Zhou, D. Li, et al.: Recognizing workers’ construction activities on a reinforcement processing area through the position relationship of objects detected by faster R-CNN, Engineering, Construction and Architectural Management, Vol. 2022 (2022), ahead-of-print.

J. Li, X. Zhao, G. Zhou, et al.: Standardized use inspection of workers’ personal protective equipment based on deep learning, Safety Science, Vol. 150 (2022), p.105689.

Jack C. P. Cheng, P. K. Y. Wong, H. Luo, et al.: Vision-based monitoring of site safety compliance based on worker re-identification and personal protective equipment classification, Automation in Construction, Vol. 139 (2022), p.104312.

B. Wang, Z. Chen, M. Li, et al.: Omni-Scan2BIM: A ready-to-use Scan2BIM approach based on vision foundation models for MEP scenes, Automation in Construction, Vol. 162 (2024), p.105384.

Y. Bao, Z. Chen, S. Wei, et al.: The State of the Art of Data Science and Engineering in Structural Health Monitoring, Engineering, Vol. 5 (2019) No.2, p.234-242.

Y Lu, H Gao, T Huo: Framework and teaching plan for intelligent construction programme, Journal of Architectural Education in Institutions of Higher Learning, Vol. 31 (2022) No.1, p.8-14.

S Jia: The Practice of Civil Engineering Talent Cultivation in the Perspective of Digital Intelligent Construction, Academy, Vol. 17 (2024) No.19, p.83-85.

S Liu, H Luo, J Sun, et al: Thoughts on the Design of Practical Teaching Plan for Understanding Intelligent Construction Program, Research in Higher Education of Engineering, Vol. 2020 (2020) No.1, p.20-24.

R. Duan, H. Deng, M. Tian, et al.: SODA: A large-scale open site object detection dataset for deep learning in construction, Automation in Construction, Vol. 142 (2022), p.104499.

Y. Cha, W. Choi, O. Buyukozturk: Deep Learning-Based Crack Damage Detection Using Convolutional Neural Networks, Computer-Aided Civil and Infrastructure Engineering, Vol. 32 (2017), p.361-378.