Intelligent Defect Diagnosis of Appearance Quality for Prefabricated Concrete Components Based on Target Detection and Multimodal Fusion Decision

Abstract

The quality of prefabricated concrete (PC) components during the construction phase is crucial for project safety. However, manual inspections are no longer sufficient to meet the demands of efficient and large-scale quality inspections of PC components. While computer vision (CV) can quickly inspect the surface quality of PC components, it fails to effectively prioritize critical quality defects among different components. Treating all quality defects equally would result in resource wastage. To address the efficient detection of external quality in PC components during the construction phase, this study proposes an appearance quality diagnosis method based on object detection and multimodal fusion decision. By integrating human and machine intelligence in quality inspections and implementing multimodal fusion decision-making, the intelligent quality diagnosis method becomes more targeted. By utilizing image object detection, the accuracy of identifying quality defects reached 87.70%. The fusion decision approach combining human and machine intelligence is applied to make informed decisions regarding structures with quality defects. Through the utilization of point cloud data, high-precision quality inspections of problematic components with an accuracy of 0.1 mm have been achieved. The developed case library enables defect tracking and provides recommendations for optimization solutions. The results demonstrate that the proposed engineering quality diagnostic method can effectively and quickly identify quality defects in PC components and provide improvement suggestions.