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.

翻译：施工进度监测（CPM）对于有效的项目管理至关重要，可确保项目按时、按预算交付。传统的CPM方法通常依赖人工检查和报告，耗时且易出错。本文提出了一种利用先进目标检测算法进行自动化CPM的新方法。该方法充分利用YOLOv8的实时检测能力和高准确性，在施工现场图像和视频中识别和追踪建筑元素。我们创建了一个包含多种建筑元素的数据集，并标注了相关对象以用于训练和验证。使用精确率、召回率和F1分数等标准指标对所提方法的性能进行了评估，结果表明其相较于现有方法有显著提升。将计算机视觉整合到CPM中，为利益相关方提供了可靠、高效且经济可行的项目进度监测手段，有助于及时决策，最终促进施工项目的成功完成。