Development of advanced progress recognition algorithms for construction monitoring.

ABSTRACT:

Introduction

The 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.

Results

A custom-labeled da

SUBMITTER: Yingdong L 

PROVIDER: S-EPMC12510549 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature