Seam Inspection Using Image Feature Extraction and Machine Learning
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Solution Overview
Problem
Visual inspection of seams in sewn products relies heavily on worker skill and condition, leading to inconsistent quality and high training costs, making it difficult to maintain consistent inspection levels and efficiency.
Innovation Solution
A seam inspection apparatus that uses image processing and machine learning to automatically determine seam quality by acquiring image data, extracting feature quantities such as length and angle, and performing quality determination using supervised learning with teacher data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If visual inspection by workers is used, then inspection can be performed with simple equipment, but inspection consistency and reliability deteriorate due to dependence on worker skill and physical condition
Solution Approach 1:
The patent replaces the mechanical visual inspection system (human workers) with an automated image processing system that captures seam images, extracts geometric features, and automatically determines quality. This substitution eliminates dependence on worker skill and physical condition, ensuring consistent and reliable inspection results without requiring complex manual training programs
Solution Approach 2:
The inspection system performs self-assessment by automatically capturing images, extracting features, and determining quality without human intervention. The system serves itself by having the image processing unit automatically analyze seam geometry and the determination unit independently make quality decisions based on extracted features, eliminating the need for human workers while maintaining high reliability
2Reliability
If visual inspection by workers is used, then equipment cost is low, but training cost and time increase to secure proficient workers
Solution Approach 1:
The patent replaces human workers with an automated system that requires no training. The image processing unit and quality determination unit are pre-programmed with algorithms to automatically assess seam quality, eliminating the time and resources needed to train workers while maintaining consistent inspection quality levels
Solution Approach 2:
The system transforms the inspection process from subjective visual assessment to objective quantitative measurement by extracting geometric parameters (length, angle, curvature) of seam lines. This parameter transformation enables automated quality determination without requiring workers to develop specialized skills, eliminating training time while ensuring reliable inspection results
3Productivity
If automated image processing is implemented, then inspection consistency improves, but device complexity and initial cost increase
Solution Approach 1:
The patent divides the inspection system into distinct functional modules: an image processing unit that captures and processes images, a feature extraction unit that identifies geometric characteristics, and a quality determination unit that makes final assessments. This segmentation allows each component to perform its specific function efficiently, improving overall productivity while managing device complexity through modular design
Solution Approach 2:
The patent introduces an image processing unit as an intermediary between the seam (object of inspection) and the quality determination (final output). This intermediary captures images, extracts relevant features, and prepares data for analysis, enabling automated high-speed inspection while keeping the overall system architecture manageable through clear separation of concerns
Data Source
AI summary
A seam inspection apparatus can automatically determine the quality of an image of a seam of a sewn product. The seam inspection apparatus includes an image data acquisition unit that acquires image data of a seam of a sewn product, a feature extraction unit that extracts a feature quantity of the seam from the image data of the seam of the sewn product acquired by the image data acquisition unit, and a quality determination unit that performs quality determination of the seam based on the feature quantity of the seam.