Intelligent Computer Vision Applications for Automated Urban Infrastructure Monitoring
Keywords:
Computer Vision, Urban Infrastructure,, Automated Monitoring, Smart Cities, Deep Learning, Image Analytics, Structural Health, Intelligent SystemsAbstract
Rapid urbanization has significantly increased the complexity and scale of infrastructure systems, creating challenges for traditional inspection and maintenance practices. Intelligent computer vision has emerged as a transformative solution for automated urban infrastructure monitoring by enabling continuous, accurate, and scalable assessment of structural health and environmental conditions. Advanced vision-based systems integrate deep learning, sensor fusion, and edge computing to detect defects, assess risks, and support proactive decision-making. This paper examines the architectural foundations, analytical workflows, and deployment strategies of intelligent computer vision applications in urban infrastructure monitoring. Emphasis is placed on system automation, real-time analytics, operational efficiency, and reliability across large-scale urban environments.
References
Cha, Y. J., Choi, W., & Büyüköztürk, O. (2016). Deep learning-based crack damage detection using convolutional neural networks. Computer-Aided Civil and Infrastructure Engineering, 32(5), 361–378.
Koch, C., & Brilakis, I. (2011). Pothole detection in asphalt pavement images. Advanced Engineering Informatics, 25(3), 507–515.
Yeum, C. M., & Dyke, S. J. (2015). Vision-based automated crack detection for bridge inspection. Computer-Aided Civil and Infrastructure Engineering, 30(10), 759–770.
Dorafshan, S., Maguire, M., & Hoffer, N. (2018). Comparison of deep convolutional neural networks and edge detectors for image-based crack detection. Construction and Building Materials, 186, 1031–1045.
Ellenberg, A., Kontsos, A., Moon, F., & Bartoli, I. (2016). Bridge deck delamination identification from unmanned aerial vehicle imagery. Journal of Bridge Engineering, 21(9), 04016037.
Kang, D., & Cha, Y. J. (2018). Autonomous UAVs for structural health monitoring using deep learning and computer vision. Automation in Construction, 87, 170–182.
Zhong, B., Ding, L., Luo, H., Zhou, Y., Hu, Y., & Hu, H. (2020). Ontology-based semantic modeling of construction safety knowledge. Advanced Engineering Informatics, 44, 101093.
Kim, S., & Sim, S. H. (2022). Vision-based structural health monitoring using deep learning: A review. Structural Control and Health Monitoring, 29(3), e2894.
Spencer, B. F., Hoskere, V., & Narazaki, Y. (2019). Advances in computer vision-based civil infrastructure inspection and monitoring. Engineering Structures, 187, 196–222.
Koch, C., Paal, S. G., Rashidi, A., Zhu, Z., König, M., & Brilakis, I. (2014). Achievements and challenges in machine vision-based inspection of large concrete structures. Advanced Engineering Informatics, 28(2), 112–126.
Valença, J., Dias-da-Costa, D., Júlio, E., Araújo, H., & Costa, H. (2013). Automatic crack monitoring using photogrammetry and image processing. Measurement, 46(1), 433–441.
Zhang, L., Yang, F., Zhang, Y. D., & Zhu, Y. J. (2016). Road crack detection using deep convolutional neural networks. IEEE International Conference on Image Processing Proceedings, 3708–3712.
Li, S., Zhao, X., Zhou, G., & Qian, Y. (2019). Automatic pixel-level multiple damage detection of concrete structures using fully convolutional networks. Computer-Aided Civil and Infrastructure Engineering, 34(7), 616–634.
Sattar, S., Li, S., & Chapman, M. (2018). Road surface monitoring using computer vision. Transportation Research Record, 2672(45), 128–139
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Muhammad Ashraf, Roman Pavel (Author)
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
