Computer Vision for Construction Progress Monitoring: A Real-Time Object Detection Approach
It addresses the time-consuming and error-prone manual inspection in construction project management, offering a more efficient solution for stakeholders, though it is incremental as it applies an existing algorithm to a new domain.
This paper tackles the problem of automating construction progress monitoring by proposing a real-time object detection approach using YOLOv8, which demonstrated significant improvements in precision, recall, and F1-score over existing methods.
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.