Color-Agnostic Material Face Detection via Edge Analysis
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing methods for identifying the correct side of materials, particularly in sewing applications, rely on human intervention and visual inspection, which can lead to errors, especially in cases where human operators are colorblind or fatigued, and may physically alter the material structure, causing issues in later manufacturing steps.
Innovation Solution
A color-agnostic material face identification system using a computing environment with a vision system, material training module, imaging parameter module, and model training module to automatically identify the correct side of materials by capturing images, determining optimal imaging parameters, and training machine learning models for accurate face prediction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If human operators perform visual inspection to identify material sides, then material orientation can be determined, but human error increases due to colorblindness and fatigue
Solution Approach 1:
The patent replaces the human visual inspection system with an automated machine vision system that captures images of material edges and uses image processing algorithms to determine material orientation. This substitution eliminates human errors related to colorblindness and fatigue while maintaining reliable identification accuracy.
Solution Approach 2:
The patent introduces image processing algorithms and machine learning models as intermediaries between the material sample and the identification result. These intermediaries automatically analyze edge characteristics and predict material orientation without requiring direct human intervention, thereby improving reliability while reducing operational complexity.
2Measurement precision
If physical handling is used to identify material sides, then orientation can be determined, but material structure may be altered
Solution Approach 1:
The patent creates a digital copy (image) of the material edge instead of physically handling the material. The image processing system analyzes this copy to determine orientation, thereby achieving precise measurement without any physical contact that could alter the material structure.
Solution Approach 2:
The patent replaces physical handling methods with an optical imaging system. By using light to capture material edge characteristics and processing these images computationally, the system achieves accurate orientation detection while completely avoiding mechanical contact that could damage or alter the material.
3Adaptability or versatility
If color-based methods are used to identify material sides, then orientation can be determined, but the method fails for colorblind operators and colorless materials
Solution Approach 1:
The patent changes the detection parameter from color-based identification to edge-based identification. By analyzing edge characteristics such as texture, pattern, and geometric features rather than relying on color, the system achieves versatility across different material types including colorless materials while maintaining broad applicability.
Solution Approach 2:
Instead of trying to make the detection method adapt to different color conditions, the patent inverts the approach by using a detection method (edge analysis) that is inherently independent of color. This inversion makes the system universally applicable to all material types without requiring color-based detection.
Data Source
AI summary
Various examples are provided related to face identification of material. An image can be captured by a vision system and feature parameters determined and compared to a material feature database to determine which face of the material is being presented. The vision system can employ a set of parameters for configuration to acquire the image. The system can communicate the identification to downstream processes in real time. A near-universal, color agnostic, angular orientation independent identification of material faces can be determined without the need for physical manipulation of the material.