AI large scale model and small model cooperation
Using industrial AI large scale models to generate defect samples, intelligent labelling, and enrichment of defect libraries.
Machine Vision Algorithm Library
Rich machine vision algorithm toolkits, flexible invocation methods, and good performance.
Deep Learning Training Platform
Using a small number of samples, an industrial Al depth learning visual detection model suitable for specific application scenarios can be trained.
Multi-scale Product Defect Library
Deeply engaged in the field of Al vision for many years, we have a large amount of industrial product defect library.
Industrial Equipment Communication Library
Support industrial equipment such as industrial cameras, 3D scanners, PLCs, and protocols such as Modbus, TCP, and serial communication.
Core Algorithm Functions
Dimensional Inspection
Measure product dimensions through positioning and quantitatively analyze product dimensions.
Object Detection
Target positioning and classification of products, commonly used for small target detection, multi-target detection, etc.
Segmentation Detection
Pixel-level inspection of products, often used for product surface scar inspection, etc.
Classification detection
Classification and inspection of products to determine whether they are good crystals or defects, often used for category judgments, etc.
3D detection
By analyzing and transforming the point cloud data, the 3D information of the object is extracted and quantitatively analyzed.
Positioning and assembly of equipment workpiece
Using the correlation fusion of 2D/3D information to achieve module assembly, inspection material positioning, placement, and appearance inspection, etc.
Platform Advantages
High precision
3C inspection Highest precision = 0.0005mm
Lithium-ion inspection Highest precision = 0.05mm
Functional defects Leakage rate = 0%
Non-functional defects Leakage rate<0.1%
All defects Minimum overkill rate <1%
High complexity
Support multi-model common line
3C inspection support one-key change type
Li-ion battery soft pack cell inspection support within 30min change type
High performance
Support 3C inspection CT < 0.3 s/pcs
High reusability
Reuse the previous generation of deep learning models for defect detection, supporting detection under the condition of 0 defective samples for new products.
Fast model training
Use AI large scale models to batch generate or label defective images, enrich the detection of small models, quickly complete the model iteration, shorten the project cycle
Application Demonstration
Hardware debugging
Integrated, unified and visualized, camera, hardware interface tools, multi-camera scheduling debugging tools to make the debugging process more convenient
Master development
In response to the diverse characteristics of industrial scenarios, TimesAl supports flexible and freely configurable display units, flow meter indicators, results query, export and upload logic
Algorithm development
Vision algorithm module with balance of generality and ease of use, visual debugging process, pre-trained defect library for different materials, developers can achieve 0 code algorithm to go online quickly
Algorithm tuning
Fast model iteration based on continuous learning, interpretable/self-learning tools for industrial scenarios, dramatically reducing model optimization time
Intelligent production management
Provide a variety of production data statistics templates, such as workpiece capacity statistics template, yield analysis template, etc., to achieve real-time supervision of production data
Version
Service
Dihuge is responsible for the detection rate of the platform and secondary development algorithm;
Dihuge provides after-sales consulting services for optical imaging effects and overall debugging, including on-site technical support, remote technical support, etc;
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