Protective Clothing Detection with Real-Time Segmented Imaging
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Solution Overview
Problem
Current inspection of cleanroom suits relies on manual visual inspection, lacking an automated solution for detecting and ensuring compliance with protective clothing guidelines and cleanliness standards in IC manufacturing environments.
Innovation Solution
A detection and identification system that includes image capturing, real-time display, and automatic marking of protective clothing regions, utilizing anomaly detection and semantic segmentation algorithms to identify and mark regions requiring attention, and integrating facial recognition and edge computing for personalized equipment recommendations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual visual inspection is used to detect protective clothing compliance, then the system is simple and easy to implement, but the detection efficiency is low and subjective errors occur
Solution Approach 1:
The patent replaces manual visual inspection with an automated image processing system using cameras, computers, and algorithms to detect protective clothing compliance. This substitution of mechanical/manual detection with automated optical and computational systems directly resolves the contradiction by dramatically improving detection efficiency while accepting increased system complexity.
Solution Approach 2:
The system enables self-service detection where the computer automatically analyzes images, identifies compliance issues, and generates reports without requiring manual inspection. The automated anomaly detection and semantic segmentation algorithms perform the detection task independently, improving productivity while the system handles its own operation.
2Measurement precision
If automated image processing is implemented, then detection accuracy and objectivity are improved, but the system complexity and cost increase
Solution Approach 1:
The patent applies semantic segmentation to divide the image into multiple regions corresponding to different body parts and protective clothing components. This segmentation approach improves detection accuracy by analyzing each region independently with specific detection criteria, while the modular nature of segmentation allows the complex task to be broken down into manageable computational steps.
Solution Approach 2:
The system introduces an intermediary computer vision processing layer between image capture and compliance determination. The anomaly detection algorithm and semantic segmentation act as intermediaries that objectively analyze the image data, improving measurement precision by removing subjective human judgment while managing complexity through standardized processing pipelines.
3Loss of time
If real-time detection and marking is implemented, then the response time is reduced and compliance issues are identified immediately, but the computational load and energy consumption increase
Solution Approach 1:
The system implements periodic detection by capturing images at specific intervals or triggers rather than continuously processing all visual data. This periodic action reduces computational energy consumption by activating the intensive image processing only when needed, while still providing timely detection and marking of compliance issues.
Solution Approach 2:
The system performs preliminary actions by pre-defining detection criteria, anomaly thresholds, and marking protocols before actual detection occurs. This preliminary preparation allows the real-time processing to focus only on comparing captured images against pre-established standards, reducing computational load during actual detection while maintaining fast response times.
Data Source
AI summary
A detection and identification system includes a display portion, an image-taking portion, and a protective clothing detection portion. The image-taking portion captures a full-body image of a person wearing protective clothing. The display portion displays the full-body image in real time. The protective clothing detection portion divides the full-body image into plural to-be-identified regions according to the joints of the human body, uses an anomaly detection method or semantic segmentation algorithm to determine whether each to-be-identified region meets detection criteria, and marks the to-be-identified regions in such a way that each to-be-identified region meeting, or failing to meet, the detection criteria is displayed by the display portion as marked with a first mark or a second mark respectively. The first and second marks are different-colored blocks or frames. The system allows the current condition of the protective clothing to be rapidly known so that appropriate handling measures can be taken.