Burr Detection Using Two-Stage Machine Learning and 3D Scanning
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Solution Overview
Problem
The existing methods for detecting specific points on objects from three-dimensional information are inefficient due to the need for high-resolution data acquisition, which increases the number of steps required for detection, making the process cumbersome.
Innovation Solution
A specific point detection system that uses an imager to acquire images, a first detector using machine learning to identify specific points, and a three-dimensional information acquirer to re-detect these points using a second detection model, reducing the number of steps and improving accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If three-dimensional information on an enlarged object is acquired to improve resolution, then measurement precision is improved, but the number of acquisition steps increases
Solution Approach 1:
The system performs preliminary detection using a first detection model on initial three-dimensional information to identify candidate specific points before acquiring enlarged three-dimensional information only at those specific locations. This preliminary action avoids the need to acquire enlarged information across the entire object, reducing the number of acquisition steps while maintaining high measurement precision at critical areas.
2Measurement precision
If three-dimensional information on an enlarged object is acquired to improve resolution, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The detection process is segmented into two distinct stages: first detection using a first detection model on initial three-dimensional information to identify candidate points, and second detection using a second detection model on enlarged three-dimensional information at those specific points. This segmentation allows the system to maintain high measurement precision while managing device complexity by dividing the detection task into manageable phases with different resolution requirements.
3Measurement precision
If conventional detection methods are used with high-resolution three-dimensional information, then measurement precision is improved, but processing time increases
Solution Approach 1:
The system applies partial action by acquiring enlarged three-dimensional information only at specific locations identified by the first detection model, rather than processing the entire object at high resolution. This reduces the volume of data requiring intensive processing while maintaining high measurement precision at the detected specific points, thereby reducing overall processing time.
Data Source
AI summary
A specific point detection system includes an imager that acquires an image of an object W, a first detector that detects, using a first detection model learned by machine learning, a burr B included in the object W by taking the image acquired by the imager as input, a three-dimensional scanner that acquires three-dimensional information on the object W including the burr B detected by the first detector, and a second detector that re-detects, using a second detection model learned by machine learning, the burr B by taking the three-dimensional information acquired by the three-dimensional scanner as input.