Machine Vision Optical Chain Defect Detection With Reflective Scanning
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Solution Overview
Problem
Machine vision systems generate erroneous images and data due to defects in their optical chains, such as scratches or dirt particles on lenses or imagers, leading to misclassification of specimen containers and specimens.
Innovation Solution
A method and apparatus that move a reflective tool through the imaging device's field of view, capturing multiple images to analyze for defects, and stitch them together to form a synthetic background for defect detection in the optical chain.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a machine vision system is used to analyze specimen containers and specimens, then automated testing efficiency is improved, but defects in the optical chain cause erroneous images and misclassification
Solution Approach 1:
The system performs preliminary defect detection by capturing images of a reflective tool at multiple locations in the field of view before actual specimen analysis. This preliminary action identifies optical chain defects (scratches, dirt particles) in advance, allowing the system to correct or compensate for these defects during subsequent specimen analysis, thereby maintaining both high productivity and reliability
Solution Approach 2:
A reflective tool is introduced as an intermediary object to detect optical chain defects. The reflective tool serves as a mediator between the imaging system and the specimen analysis process, enabling the system to identify and account for optical imperfections without directly analyzing specimens, thus ensuring accurate specimen classification while maintaining automated efficiency
2Measurement precision
If multiple images are captured and analyzed to detect defects, then defect detection accuracy is improved, but system complexity increases
Solution Approach 1:
The defect detection process is segmented into discrete steps: capturing multiple images at different field of view locations, processing each image individually to identify defects, and then combining results to form a comprehensive defect map. This segmentation allows the system to achieve high measurement precision through multiple measurements while managing complexity by breaking down the overall process into manageable, modular operations
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Accurately identifies defects in the optical chain, ensuring precise analysis of specimen containers and specimens by correcting for errors caused by optical chain anomalies.
Implementation Method 1
moving a reflective tool through the first field of view; capturing a plurality of images of the reflective tool at different locations in the first field of view
Data Source
AI summary
Methods of identifying a defect in a machine vision system. Embodiments of the method include providing a first imaging device having a first field of view; moving a reflective tool through the first field of view; capturing a plurality of images of the reflective tool at different locations in the first field of view using the first imaging device; and analyzing at least one of the plurality of images to identify one or more defects in the machine vision system. Systems and apparatus configured to carry out the methods are provided, as are other aspects.