Computer Vision for Building Component Segmentation

The patent replaces manual mechanical inspection processes with an automated computer vision system that uses machine learning models to detect, segment, and classify building components. The system processes images and video feeds to automatically identify structural elements, materials, and conditions without human physical presence, thereby eliminating human error while maintaining assessment accuracy and significantly reducing inspection time.

The patent employs multiple machine learning models with different specialized functions (detection models, segmentation models, classification models) that process images through various transformation stages. Each model is trained on specific parameters and features, progressively refining the analysis from basic object detection to detailed material classification, thereby achieving both high automation and high precision simultaneously.

The patent creates digital copies of building components through image capture and processing, replacing physical inspection and manual documentation. The system generates structured digital data including detected objects, segmented regions, classified materials, and measured attributes directly from images, eliminating the need for physical presence and manual paperwork while maintaining complete information capture.

The patent segments the inspection task into distinct processing stages: detection of building components, segmentation of specific regions within components, classification of materials and attributes, and measurement of physical properties. This segmentation allows each stage to be optimized independently with specialized machine learning models, improving reliability through consistent automated processing while maintaining operational simplicity through integrated system execution.