ArticlePloS one2025
Development of advanced progress recognition algorithms for construction monitoring.
Article in PloS one, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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4 authors.
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Abstract
introductionThe traditional methods of Construction Progress Monitoring (CPM) involve manual inspection and reporting, which are slow, error-prone, and labor-intensive. PURPOSE: This study aims to introduce a novel, automated approach for CPM using YOLOv8, a state-of-the-art object detection algorithm, to enhance efficiency and accuracy in monitoring construction projects. METHODOLOGY: YOLOv8 is employed for its real-time processing capabilities and high precision, making it suitable for identifying and tracking construction elements in images and videos captured on-site. This study creates a comprehensive dataset of construction images and videos to assess and validate the proposed method with meticulous labeling of relevant objects.
resultsA custom-labeled dataset of 768 images of window installation stages was developed and used to train the model. The proposed YOLOv8 model achieved a mean Average Precision (mAP@50) of 0.953, mAP@50-95 of 0.678, precision of 0.91, and recall of 0.88. This integration of computer vision into CPM offers substantial benefits, including reliable, efficient, and cost-effective progress monitoring. INNOVATION: This approach presents an innovative computer vision application in construction progress monitoring. It facilitates timely decision-making throughout the project lifecycle and offers a practical alternative to manual CPM methods. Using YOLOv8 for automated CPM is a novel contribution to construction project management, potentially impacting the successful completion.
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