Language

Single-sided AI Artificial Intelligence Detection Equipment

≤5

Minimum sample size

>99.99%

Coincidence

>30

Number of compatible models

99%

Automation

AI camera

Real-time monitoring

OCR inspection

Quality inspection

Product Parameters

Small sample training： Reduce the excessive dependence of AI algorithm on data size.In the detection of common types of defects, it can realize quick training and deployment on the basis of a small amount of data, winning rather high detection accuracy
Transfer learning： Enhance the universality of neural network model.Based on the extraction and training of common features of products made of the same material, improve the detection universality of neural network model, transfer the model to more product models and solve the application scenario problems of fewer samples and more models

Visual debugging： Solve the black box problem generated by the neural network under the module "Classification" of deep learning algorithm, and guide the optimization of neural network model through visual thermodynamic diagram
Hardware accelerating optimization： Fully accelerate the optimization of software and hardware. On the basis of ensuring the detection accuracy and precision, it can detect small articles much faster than the traditional algorithms.

Image feature enhancement： Effectively improve the detection accuracy by multi-dimensional feature extraction Conduct the multi-dimensional feature extraction for training set images, auto-filter the effective feature information and increase the training weight of such data to effectively improve detection accuracy.Conduct the multi-dimensional feature extraction for training set images, auto-filter the effective feature information and increase the training weight of such data to effectively improve detection accuracy
Precise feature recognition： By optimizing the underlying framework of the algorithm, the detected target area is infinitely coincident with the detection result, so as to improve the detection accuracy and greatly reduce the rate of over detection and missed detection

Our Advantages

Carry out the pixel level detection for the tested materials, such as identification of silicon wafer crack area, impact bearing area, etc.

Classify and judge the tested materials, such as OK/NG classification of materials, color and type of tested objects, and fine classification of 3C defects

Classify and position the detected materials. Applicable to detection, counting, etc. of the multiple/small objects, including drug pill counting, 3C electronic component detection and other scenarios

Tag and recognize single character and multiple characters.Characters under different backgrounds can be recognized, for example, the character information of products or components in various complex scenarios, such as steel seals and laser engravings

Technical Proposal

Applicable Scenarios

Single-sided AI Artificial Intelligence Detection Equipment

StarsPlas takes industrial vision and artificial intelligence algorithm as the core and focuses on the research and development of standardized AI visual detection software platforms, Al intelligent cameras and intelligent hardware, Al intelligent data analysis systems and overall solutions in the industrial field.
StarsPlas has a comprehensive layout in both domestic and international markets, with the goal of providing standardized industrial AI visual algorithm platforms and overall solutions for the fields and industries of pan-3C products, pan-semiconductors, PV, automobiles and parts, improving the product quality management level, enhancing the production efficiency, strongly boosting the improvement of product yield and productivity of the factories and deeply empowering the upgrading of the intelligent manufacturing industry.