Image Segmentation Defect Detection With Severity and Confidence
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Solution Overview
Problem
Existing fabric maintenance processes rely heavily on subjective expert opinions, leading to inconsistent defect detection and labor-intensive manual surveys, which can result in incorrect characterization and significant operational risks due to variable results and sampling bias.
Innovation Solution
An automated method and system for detecting physical features using image segmentation processes, including neural networks, to identify and classify defects in objects, with confidence factors and severity metrics, and integrating metadata for enhanced accuracy and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual inspection by subject matter experts is used, then detailed assessment of defects can be performed, but the results are inconsistent and subject to sampling bias
Solution Approach 1:
The patent replaces the mechanical system of manual visual inspection by subject matter experts with an automated image processing system using machine learning algorithms. The system captures images of the fabric and automatically detects, segments, and characterizes defects through computational analysis, eliminating human subjectivity and sampling bias while maintaining high detection accuracy through trained algorithms that consistently identify defect patterns.
2Reliability
If manual surveys are conducted regularly, then defect detection can be performed, but the process is time-consuming and labor-intensive
Solution Approach 1:
The patent implements a self-service inspection system where the fabric itself is captured by imaging devices and the defects are automatically detected and characterized by the machine learning system without requiring human operators to physically examine each fabric piece. The system processes multiple images simultaneously and automatically generates inspection reports, dramatically increasing throughput while maintaining reliable defect detection capability.
3Measurement precision
If comprehensive inspection of all fabric is performed manually, then all defects can be detected, but the cost and time requirements become prohibitive
Solution Approach 1:
The patent enables continuous inspection by implementing an automated system that can process fabric images continuously without interruption. The machine learning model analyzes images in real-time as they are captured, providing continuous defect detection coverage across all fabric pieces passing through the inspection system, eliminating the need for periodic manual sampling and achieving comprehensive inspection coverage without time loss.
Data Source
AI summary
A computer can operated, including detecting defects, or other physical features, of artificial objects. Image data is received of one or more artificial objects, and applying an image segmentation process to the image data to detect predetermined defects of the one or more artificial objects. The image segmentation process identifies one or more regions of the image data determined to have a likelihood of showing one or more of the predetermined defects. The identified one or more regions is output. The image segmentation process determines severity metrics for the defects in the one or more regions, wherein a severity metric represents a severity or significance of a defect. The image segmentation process further determines a confidence factor for each region of the one or more regions, wherein the confidence factor represents a likelihood of the presence of a predetermined defect in the region.