Color-Pattern Tool Identification With Sensor Calibration
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Solution Overview
Problem
Existing automated tool control systems face challenges in accurately identifying color-coded tags due to inconsistent color/hue values across batches, leading to misidentification of tools, especially when using cameras for image analysis.
Innovation Solution
Implement a calibration process to adjust color gain and boundary ranges for color recognition, utilizing image sensors to correlate pixel patterns with known color parameters, and adjust RGB gains to ensure accurate identification of color-coded tags.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If color-coded tags are used to identify tools, then tool identification can be performed visually, but color/hue values shift between batches causing misidentification
Solution Approach 1:
The system transforms color identification from direct hue value matching to pattern recognition based on relative color relationships. By changing the parameter from absolute hue values to relative color patterns (e.g., which stripe is brightest, darkest, or has intermediate brightness), the system becomes invariant to batch-to-batch color shifts while maintaining identification accuracy.
Solution Approach 2:
Instead of trying to make color values consistent across batches (the conventional approach), the invention inverts the problem by making the identification system insensitive to color value variations. The system determines tool identity based on the relative relationships between color stripes rather than their absolute values, effectively solving the problem from the opposite direction.
2Extent of automation
If camera imaging is used to detect tool presence, then automated detection can be performed, but color inconsistencies cause misidentification of tools
Solution Approach 1:
The system changes the detection parameter from absolute color/hue values to relative color patterns. By analyzing which stripes have brighter or darker intensities relative to each other rather than comparing absolute color values, the automated detection becomes reliable even when batch-to-batch color variations occur.
Solution Approach 2:
The system incorporates a feedback mechanism where reference images of tools with their color patterns are stored, and detected tool images are compared against these references. The pattern recognition algorithm uses this feedback to identify tools based on their relative color stripe patterns, compensating for color inconsistencies through iterative pattern matching.
3Measurement precision
If color gain adjustment is implemented, then color recognition accuracy can be improved, but system complexity increases
Solution Approach 1:
The system implements self-service calibration by automatically determining color patterns from captured images without requiring manual intervention. The processor autonomously analyzes the relative brightness of different color stripes, compares them against stored reference patterns, and identifies tools based on these comparisons, eliminating the need for complex manual calibration procedures.
Solution Approach 2:
The system creates simplified copies of color information by extracting only the essential pattern features (relative brightness relationships) from full-color images. Instead of processing complete color data with all its variations, the system copies and stores only the critical pattern information needed for identification, reducing computational complexity while maintaining accuracy.
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
Enhances the accuracy of tool identification by correcting for color inconsistencies, ensuring precise recognition and reducing misidentification of tools in storage containers.
Implementation Method 1
at least one image sensing device configured to capture image data of the plurality of storage locations
Data Source
AI summary
The disclosure comprises systems and methods for identifying color-coded tags on tools in a storage container where the identification sensor can be calibrated and adjusted. The method can include capturing image data comprising a plurality of pixels of an identification tag associated with a tool in a storage container. The method can include correlating a plurality of pixels to a numeric hue value. The method can include identifying a pattern of pixels from the plurality of pixels, wherein the pattern of pixels is correlated to a known pattern of color parameters consistent with the identification tag associated with the tool in the storage container. Further in response to identifying the pattern of pixels is consistent with the identification tag, determining the presence or absence of the tool in the storage container. The method can further include implementing a color gain to adjust a boundary range for a color.