Field imaging framework for morphological characterization of aggregates with computer vision: Algorithms and applications

Researchers developed a comprehensive field imaging framework using computer vision and AI to automatically characterize construction aggregates like sand, gravel, and stone. The system uses 2D image analysis and 3D point cloud reconstruction with machine learning to replace manual inspection methods in construction material assessment.

• →New AI-powered framework automates construction aggregate analysis, replacing manual visual inspection methods.
• →System combines 2D image segmentation with 3D reconstruction and shape completion using neural networks.
• →Framework handles multiple scenarios from individual aggregates to complex stockpile analysis.
• →Researchers created synthetic datasets and 3D particle libraries to train machine learning models.
• →Technology demonstrates practical applications in real-world stockpile analysis with ground-truth validation.