Target Detection Technology of Mechanical, Electrical, and Plumbing Components Based on CV
Source abstract
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: crossref · Quality: baseline
Research context
OpenAlex topics: Industrial Vision Systems and Defect Detection
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of System Design and Intelligent Manufacturing, Southern University of Science and Technology, Shenzhen 518051, China
Queensland College of Art and Design, Griffith University, Brisbane 4101, Australia
National Key Laboratory of Wireless Communications, University of Electronic Science and Technology of China, Chengdu 611731, China
Funding
Shenzhen-based Universities
References (34)
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