Edge AI Visual Inspection for Secure Defect Detection
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Solution Overview
Problem
Existing visual inspection systems face challenges in implementing AI-based defect detection in manufacturing settings due to high costs, complexity, and the need for significant hardware resources. Additionally, human visual inspection is limited in accuracy and speed, and often requires multiple inspection steps, increasing time and cost.
Innovation Solution
A system and method for visual inspection that includes a camera, a node computing device with a defect detection model for analyzing image data, and a programmable logic controller (PLC) to determine defect acceptability. The system can perform local inference and is designed for use in quality assurance stations, allowing for autonomous movement of the camera and integration with robotic arms for efficient inspection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If cloud-based AI visual inspection systems are used, then defect detection capability is improved, but data security control deteriorates and communication dependency increases
Solution Approach 1:
The patent transitions the AI inference location from cloud-based (external dimension) to edge-based (local dimension), enabling defect detection capabilities to be maintained while data remains within the factory network boundaries, thus securing data control while preserving detection accuracy
2Reliability
If on-premise servers with GPUs are used for local AI inference, then data security control is improved, but hardware cost and power consumption increase
Solution Approach 1:
The patent replaces expensive, high-power GPUs with lower-cost, lower-power edge computing devices that are sufficient for AI inference tasks, reducing both hardware investment and ongoing power consumption while maintaining local data security control
Solution Approach 2:
The patent optimizes AI model parameters and computing resources to match the capabilities of edge devices, enabling efficient defect detection with reduced hardware requirements and lower power consumption compared to full-featured GPU servers
3Ease of manufacture
If traditional visual inspection systems are used, then setup cost is reduced, but inspection speed and accuracy deteriorate
Solution Approach 1:
The patent replaces manual mechanical inspection processes with automated AI-based visual inspection systems that use cameras and edge computing, significantly increasing inspection speed and accuracy while keeping setup costs moderate through the use of standard edge devices rather than specialized hardware
4Measurement precision
If multiple inspection steps (primary, secondary, tertiary) are used, then inspection accuracy is improved, but inspection time increases
Solution Approach 1:
The patent merges multiple sequential inspection steps into a single integrated AI-based inspection process that performs comprehensive defect analysis in one pass, maintaining high accuracy while dramatically reducing the time required compared to multiple separate inspection stages
Solution Approach 2:
The patent performs preliminary AI-based defect detection and classification in a single inspection pass, eliminating the need for subsequent secondary and tertiary inspection steps, thus maintaining accuracy while reducing total inspection time
Data Source
AI summary
Provided is a system and method for visual inspection. The system may be used in a quality assurance station at a manufacturing facility or site. The system may evaluate and determine the quality of manufactured or fabricated articles. The system may include a mechanical subsystem for capturing images of the article. The system may include a sensor such as a camera for capturing data, such as images. The system may include an artificial intelligence system to determine if the article suffers from an impermissible defect.