Abstract
Construction progress monitoring (CPM) is essential for effective project management, ensuring on-time and on-budget delivery. Traditional CPM methods often rely on manual inspection and reporting, which are time-consuming and prone to errors. This paper proposes a novel approach for automated CPM using state-of-the-art object detection algorithms. The proposed method leverages e.g. YOLOv8's real-time capabilities and high accuracy to identify and track construction elements within site images and videos. A dataset was created, consisting of various building elements and annotated with relevant objects for training and validation. The performance of the proposed approach was evaluated using standard metrics, such as precision, recall, and F1-score, demonstrating significant improvement over existing methods. The integration of Computer Vision into CPM provides stakeholders with reliable, efficient, and cost-effective means to monitor project progress, facilitating timely decision-making and ultimately contributing to the successful completion of construction projects.
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Acknowledgements
We would like to express our appreciation to Mr. Fang Jian for his contribution to the project. His knowledge and dedication have been instrumental in advancing our understanding of the subject matter and achieving our research objectives. The publication is part of the research project entitled “iECO – Intelligence Empowerment of Construction Industry” which receives funding from Bundesministerium für Wirtschaft und Klimaschutz (BMWK) based on a resolution of the German Bundestag. Authors gratefully acknowledge the support and funding from the BMWK. The content of this publication reflects the author view only and the BMWK is not responsible for any use that may be made of the information it contains.
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Yang, J., Wilde, A., Menzel, K., Sheikh, M.Z., Kuznetsov, B. (2023). Computer Vision for Construction Progress Monitoring: A Real-Time Object Detection Approach. In: Camarinha-Matos, L.M., Boucher, X., Ortiz, A. (eds) Collaborative Networks in Digitalization and Society 5.0. PRO-VE 2023. IFIP Advances in Information and Communication Technology, vol 688. Springer, Cham. https://doi.org/10.1007/978-3-031-42622-3_47
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