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By: Rathan P and Sreenivas Padala S.P..
1. Student, Department of Civil Engineering, M. S. Ramaiah University of Applied Sciences, Bangalore, Karnataka, India
2. Assistant Professor, Department of Civil Engineering, M. S. Ramaiah University of Applied Sciences, Bangalore, Karnataka, India
The construction industry is witnessing a transformative shift with the adoption of computer vision (CV) technologies for productivity monitoring and performance evaluation. This review consolidates the advancements in CV applications, focusing on areas, such as real-time progress tracking, resource management, and safety monitoring. The analysis highlights how automated systems, powered by deep learning models and object detection algorithms, are replacing traditional manual methods, offering enhanced accuracy, efficiency, and decision-making capabilities. Despite these advancements, challenges, such as scalability, variability in construction environments, and limited system integration remain barriers to widespread adoption. Furthermore, critical gaps, including the limited use of advanced CV models for multi-resource tracking and the lack of intuitive interfaces for real-time visualization, are identified. This study underscores the necessity for further innovation and research to address these limitations, enabling the construction industry to harness the full potential of CV technologies. The findings offer valuable insights into how CV can drive a data-centric approach to safer and more efficient construction practices.
Citation:
Refrences:
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