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

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.