AI Vision Inspection System

AI replaces manual labor for industrial inspection, covering various fields of production manufacturing such as quality inspection, material traceability, behavior recognition, and vision guided assembly.

Data Center | Training Center | Inference Center | Application Center

Pain Points
User Value
Core Advantages
Application Scenarios
Cases
Functional Modules

Pain Points

1. Product quality requirements are increasing

Market competition intensifies, customers demand higher product quality;

2. Process and quality inspection rely on manual labor

Product design and processes are becoming more complex, traditional methods cannot meet requirements, heavily relying on manual labor;

3. Low inspection efficiency, high labor intensity

In complex scenarios, personnel find it difficult to identify product defects, time-consuming and laborious, accuracy decreases;

4. High labor cost for inspection

Labor resources are scarce, enterprise labor costs increase year by year

User Value

Significantly improve product quality

In complex scenarios, multi-classification accuracy is 10% better than the average personnel level;

Reduce manual cost for judgment

Labor for defect detection and quality judgment reduced by more than 70%;

Greatly improve defect discrimination speed

AI defect discrimination speed is 5-10 times faster than manual;

Reduce hardware resource investment

Through elastic resource scheduling, improve server efficiency, reduce hardware resource investment

Core Advantages

Multiple application scenarios
Multi-task, composite model toolkit, covering various industrial inspection application scenarios; microservices architecture, enabling no-code algorithms, loose coupling, improving product applicability; supports multi-scenario deployment on cloud, edge, and end.
Fast project delivery
Zero-code platform, customers self-modeling, quickly respond to business needs; automatic labeling, reduce 75% manual work; transfer learning, reduce 50%-70% data requirements.
Good product effect
In complex scenarios, multi-classification accuracy is 10% better than the average personnel level; transfer learning, few-shot learning, and anomaly detection technology solve the problem of difficult collection of tail anomaly samples.
Save resource costs
Self-developed high-performance AI computing engine, achieving hardware resource reduction of more than 50%; co-developed edge computing chips with well-known brands, reducing hardware costs by more than 30%.

Application Scenarios

Feeding inspection

Incoming material identification: ensure zero misuse in logistics; incoming material quality inspection: ensure zero defects in incoming materials

Personnel operations

Efficiency improvement: operation training, operation method correction; personnel safety: abnormal behavior, violation monitoring, etc.

Production line production

Intelligent production: precision processing, vision guided assembly, disordered material grasping, robot group collaborative production

Quality inspection

Quality control, defect identification and discrimination; real-time quality anomaly root cause analysis

Outbound shipping

Process traceability: connect production, logistics, and after-sales links, achieve digitization and full connectivity of intermediate processes

Cases

Learn more cases >

GETECH AI Vision Product achieves reliable and fast surface defect detection

Customer: world-leading semiconductor display enterprise

Effect: It is the first AI application implemented in the semiconductor display industry. With AOI equipment and algorithm models, it's like giving the production line 'eyes' and a 'brain'. Through AI technology for defect identification, it not only greatly improves defect detection efficiency but also eliminates issues like missed inspections and misjudgments. 1. Compared to traditional manual judgment, AI recognition speed increases 5-10 times, reducing manpower by 50%. 2. Defect identification accuracy increases from 85% to over 90%. 3. Anomaly interception time is advanced by 1-2 hours, and production cycle at inspection stations is shortened by 60%.

GETECH AI Vision Product replaces manual real-time re-judgment of product defects

Customer: air conditioning manufacturing enterprise SMT production line Product model: Getech_Dubhe-SMT-V1.0

Effect: After implementing GETECH AI system, the proportion of manual re-judgment drops below 5%, greatly saving labor costs, each factory can achieve production cost down benefits of millions per year.

Functional Modules

Standardized data format, unified data access channels, quickly complete training dataset creation;

Containerized isolation management of algorithm runtime environment, visual parameter adjustment, model training and result evaluation, easily build models;

One-click model deployment, quickly complete model delivery; multi-task computing resource automatic scheduling, fully utilize GPU computing resources;