Zebra Aurora ™ DEEP LEARNING Deep Learning Development Environmental Tool
Validge
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About This Product
Automatic AI exterior inspection in the company is low at low cost
Easy to develop just by reading the image
This add -on product is Aurora ™ Vision to respond to problems that are difficult to solve with conventional image processing algorithms.
It is an industrial quality deep learning tool that can be used to further strengthen Studio.
The actual realization by combining features and abnormal detection, advanced optical character recognition (OCR) technology, and deep learning function
The system can recognize the use of image information in the field, and to evolve the system accordingly.
Aurora ™ DEEP LEARNING is a large -scale new newly designed and optimization designed and optimized by ZEBRA's research team for industrial inspection systems.
I am using a ralnetwork.
･ Learn from a small number of samples
In general applications
Learn with 20 to 50 images for learning
I can.
Those who have more sample images
Is better, but aurora ™
Deep Learning software
Lord from a limited training set
Learn the key characteristics and have effective trays
A new system is new for ning
Equipped with a function to generate thousands of cappules
I'm sure.
･ Operation with GPU and CPU
For effective training
You need the latest GPU.
In the actual machine, either GPU or CPU
Can be used.
With a GPU, compared to CPU
Usually 3 to 10 times faster.
(Object classification is an exception, CPU
But it's the same speed).
･ Guarantee high performance
Typical learning time on GPU is usually
5-15 minutes.
Progenon time is tools and hardware
Depending on the image, 5ms per image
It is about 100ms.
Our industrial inference engine
Use Aurora ™ Weaver
So, guarantee the best performance
increase.
Application Examples
･ Features (mode with teachers)
Specify pixels corresponding to defects in the learning image (labeled)
It is a method to learn. It can be detected accurately.
･ Abnormal detection (mode without semi -teacher)
Learning is simpler. Defects are not strictly defined, but good
Learn only with a sample and look for a deviation from a good product.
Aurora ™ Deep Learning offers two methods. The first one
The second is one class of all parts of the input image, using image reconstructive technology.
Execute the classification. If you need a high -precision defective heat map, calculate
We recommend the first method at the expense of time.
･ An object classification
Traditional image processing can also be difficult classifications.
･ Instance segmentation
The instance split is a single or multiple objects in the image
It is used to identify the position of the) and divide and classify. Special
Unlike the encoding, to detect individual objects,
Even if the vehicle comes in contact or overlaps, it can be separated
to come.
･ Point glocation
Specific shapes, features that can be identified as points on the input image,
Or look for a mark. With conventional template matching
It may be compared, but this tool is multiple samples
Because it is learned in, Robasty for major fluctuations in the object
Is high.
･ OCR (Optical Character Recognition)
Character recognition (OCR) machine included in Aurora ™ DEEP Learning
Noh is complicated and uneven background, blurred characters, damaged characters,
Due to distorted characters, unclear characters, reflected metal surface, etc.
Difficult character recognition so that conventional OCR technology cannot be used
The answer to the logect.
This tool uses thousands of different image samples in advance.
It comes with a neural netet that can be used immediately
vinegar.
This tool is immediately, even if you handle very difficult cases
Up to 97% accuracy can be achieved, the user is a machine
Rough in just a few steps without the need for vision expertise
You can create an OCR application.
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Product
Zebra Aurora ™ DEEP LEARNING Deep Learning Development Environmental Tool
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