Automated Product Inspection Using Multi-Model Deep Learning
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Solution Overview
Problem
Conventional technologies lack efficient methods for analyzing and evaluating the quality of manufactured products, such as computers, as they fail to automatically detect distinguishing features, extract high and low-level features, and provide natural language descriptions, leading to time-consuming and inaccurate manual inspections.
Innovation Solution
A system utilizing a first machine learning system to generate test subject features, a second system to analyze and detect distinguishing features, and a third system for natural language processing to create evaluation information, providing an automated and accurate evaluation of the test subject based on these features.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual inspection is used to evaluate product quality, then inspectors can detect defects, but the process is time-consuming and relies on inspector skill and sharpness
Solution Approach 1:
The patent replaces manual mechanical inspection with an automated computer-based inspection system that captures images of products and uses machine learning models to detect defects, eliminating the need for human inspectors to manually examine each product while maintaining or improving detection accuracy
Solution Approach 2:
The inspection system performs self-evaluation by automatically analyzing product images through trained machine learning models, enabling the system to independently detect defects without human intervention and significantly reducing inspection time
2Reliability
If manual inspection is used to evaluate product quality, then inspectors can provide quality control, but the process relies on inspector skill and sharpness
Solution Approach 1:
The patent replaces human inspector judgment with automated machine learning-based analysis, ensuring consistent and reliable quality control decisions that do not vary based on individual inspector skill levels or fatigue
Solution Approach 2:
The system transforms subjective quality assessment into objective parameter-based evaluation by analyzing specific image features and defect characteristics through machine learning models, making the inspection process more standardized and reliable
3Loss of information
If conventional technology is used for product evaluation, then basic inspection can be performed, but automatic detection of distinguishing features and natural language descriptions are not provided
Solution Approach 1:
The patent divides the complex inspection task into multiple specialized machine learning models: one for generating product features, another for detecting distinguishing features, and a third for natural language processing, allowing each component to focus on a specific function while collectively providing comprehensive product evaluation
Solution Approach 2:
The system introduces machine learning models as intermediary components between image capture and final evaluation, enabling automatic extraction and description of product features without requiring complex direct processing of raw images
Data Source
AI summary
A method is used in evaluating a test subject in computing environments. A first machine learning system generates test subject features. A second machine learning system analyzes the test subject to detect distinguishing features of the test subject. A third machine learning system performs natural language processing on the test subject features to create evaluation information associated with the test subject. A test subject evaluation system provides an evaluation of the test subject based on the distinguishing features and the evaluation information.