Work

Target Detection Technology of Mechanical, Electrical, and Plumbing Components Based on CV

Year: 2025

Type: article

Abstract: Mechanical, electrical, and plumbing (MEP) systems are vital in construction engineering as their installation quality significantly impacts project success. Traditional inspection methods often fail to ensure compliance with building information models (BIMs), leading to safety hazards due to deviations during construction. Spurred by these concerns, this paper introduces a novel BIM-based pipeline construction comparison system that relies on computer vision technology. The developed system uses deep learning algorithms for real-time data collection to enhance monitoring efficiency and accuracy, as well as advanced object detection algorithms to compare real-time construction images with BIMs. The proposed architecture addresses the limitations of existing techniques in handling MEP complexities, and through an automatic comparison and verification process, it detects deviations promptly, ensuring adherence to design specifications. This study innovatively integrates real-time data collection, deep learning algorithms, and an automated BIM comparison mechanism to enhance the accuracy, efficiency, and automation of pipeline installation monitoring, addressing the limitations of existing inspection methods.

Source: Applied Sciences

DOI: 10.3390/app15052803

Authors: Guohua Wei, Ding Zhou, Xiaojun Yuan

Institutions: Southern University of Science and Technology, Griffith University, University of Electronic Science and Technology of China

Language: English

Topic: BIM and Construction Integration

Subfield: Building and Construction

Field: Engineering

Domain: Physical Sciences

SDGs: Industry, innovation and infrastructure, Sustainable cities and communities

Open Access status: gold

Estimated APC paid: $2,896