Machine Learning for 3D Object Detection
We developed a machine learning system that uses photogrammetry and computer vision to create high-fidelity 3D models of industrial objects. The system accurately identifies objects from over 1,000 categories, even under challenging conditions like low visibility or partial damage.
Photogrammetry and structure from motion algorithms for 3D model creation
Convolutional Neural Networks (CNN) for object classification
Pixel pattern matching and opacity variance detection
Over 1,000 categories for industrial equipment identification
Challenge
Manually identifying industrial objects, especially when they are damaged or obscured, was slow and prone to errors. An automated solution was needed for faster, more reliable identification.
Solution
Using machine learning and advanced image analysis, we developed a solution that builds 3D models from multiple angles and classifies objects based on distinguishing features like shape and texture.
Results
The solution achieved 7% higher classification accuracy compared to manual methods, significantly improving equipment audits and inventory management while reducing misidentification events.
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