Computer Vision for Construction Progress Monitoring: A Real-Time Object Detection Approach

TL;DR

This paper introduces a real-time object detection approach using advanced computer vision algorithms to automate construction progress monitoring, improving accuracy and efficiency over manual methods.

Contribution

It presents a novel automated CPM method leveraging YOLOv8 for real-time detection and tracking of construction elements, with a custom dataset and performance evaluation.

Findings

Significant improvement in detection accuracy over existing methods

Effective real-time identification of construction elements

Enhanced decision-making capabilities for project management

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.