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EasyLocate

Deep Learning localization and classification library

At a glance
  • Localization and identification of objects/products/defects
  • Counting of objects
  • Supports data augmentation and masks
  • Compatible with CPU and GPU processing
  • Includes the free Deep Learning Studio application for dataset creation, training and evaluation
  • Only available as part of the Deep Learning Bundle



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What Is Deep Learning ?

Neural Networks are computing systems inspired by the biological neural networks that constitute the human brain. Convolutional Neural Networks (CNN) are a class of deep, feed-forward artificial neural networks, most commonly applied to analyzing images. Deep Learning uses large CNNs to solve complex problems difficult or impossible to solve with so-called conventional computer vision algorithms. Deep Learning algorithms may be easier to use as they typically learn by example. They do not require the user to figure out how to classify or inspect parts. Instead, in an initial training phase, they learn just by being shown many images of the parts to be inspected. After successful training, they can be used to classify parts, or detect and segment defects.


Why Choose Open eVision’s Deep Learning Bundle?
Why Choose Open eVision’s Deep Learning Bundle?

  • Deep Learning Bundle has been tailored, parametrized and optimized for analyzing images, particularly for machine vision applications.
  • Deep Learning Bundle has a simple API and the user can benefit from the power of deep learning technologies with only a few lines of code.
  • Try before you buy: Deep Learning Bundle comes with the free Deep Learning Studio training and evaluation application.
EasyClassify, EasySegment and EasyLocate cannot be purchased separately. They are only available as part of the Deep Learning Bundle.
Download and evaluate Deep Learning Bundle using Deep Learning Studio today, and feel free to call Euresys’ support should you have any question.


Deep Learning Bundle Feature Comparison
Deep Learning Bundle Feature Comparison


Developed with the support of the DG06 Technology Development Department
Developed with the support of the DG06 Technology Development Department


EasyLocate Description
EasyLocate Description

EasyLocate is the localization and identification library of Deep Learning Bundle. It is used to locate and identify objects, products, or defects in the image. It has the capability of distinguishing overlapping objects and, as such, EasyLocate is suitable for counting the number of object instances. In practice, EasyLocate predicts the bounding box surrounding each object, or defect, it has found in the image and assigns a class label to each bounding box. It must be trained with images where the objects or defects that must be found have been annotated with a bounding box and a class label.


Deep Learning Studio
Deep Learning Studio

Open eVision includes the free Deep Learning Studio application. This application assists the user during the creation of the dataset as well as the training and testing of the deep learning tool. For EasySegment, Deep Learning Studio integrates an annotation tool and can transform prediction into ground truth annotation. It also allows to graphically configure the tool to fit performance requirements. For example, after training, one can choose a tradeoff between a better defect detection rate or a better good detection rate.


Sample Dataset: Electronic components
Sample Dataset: Electronic components

Our "Electronic Component" dataset shows how EasyLocate is able to reliably detect and count different kinds of standard electronic components stored in bulk inside plastic bags, in spite of the poor lighting conditions.


Data Augmentation
Data Augmentation

Deep Learning works by training a neural network, teaching it how to classify a set of reference images. The performance of the process highly depends on how representative and extensive the set of reference images is. Deep Learning Bundle implements “data augmentation”, which creates additional reference images by modifying (for example by shifting, rotating, scaling) existing reference images within programmable limits. This allows Deep Learning Bundle to work with as few as one hundred training images per class.


Performance
Performance

Deep Learning generally requires significant amounts of processing power, especially during the learning phase. Deep Learning Bundle supports standard CPUs and automatically detects Nvidia CUDA-compatible GPUs in the PC. Using a single GPU typically accelerates the learning and the processing phases by a factor of 100.


Neo Licensing System
Neo Licensing System

  • Neo is the new Licensing System of Euresys. It is reliable, state-of-the-art, and is now available to store Open eVision and eGrabber licenses.
  • Neo allows you to choose where to activate your licenses, either on a Neo Dongle or in a Neo Software Container. You buy a license, you decide later.
  • Neo Dongles offer a sturdy hardware and provide the flexibility to be transferred from a computer to another.
  • Neo Software Containers do not need any dedicated hardware, and instead are linked to the computer on which they have been activated.
  • Neo ships with its own, dedicated, Neo License Manager, which comes in two flavours: an intuitive, easy to use, Graphical User Interface and a Command Line Interface that allows for easy automation of Neo licensing procedures.


Software
Host PC Operating System
  • Open eVision is a set of 32-bit and 64-bit libraries that require a processor compatible with the SSE2 instruction set.
  • Deep Learning Bundle is only available in the 64-bit Open eVision library.
  • Open eVision can be used on the following operating systems:
    • Windows 10 (32- and 64-bits)
    • Windows 8 (32- and 64-bits)
    • Windows 7 (32- and 64-bits)
  • Since Open eVision 2.6, discontinued support of:
    • Windows Vista 32-bits Service Pack 1
    • Windows XP 32-bits Service Pack 3
    • Windows Embedded Standard 2009 32-bits
  • The Open eVision installer does not allow installation on virtual machines.
  • Minimum requirements:
    • RAM: 8 GB
    • Display size: 800 x 600. 1280 x 1024 recommended.
    • Color depth: 16 bits. 32 bits recommended.
    • Between 100 MB and 2 GB free hard disk space for libraries, depending on selected options.
APIs
  • Supported Integrated Development Environments and Programming Languages:
    • Microsoft Visual Studio 2008® SP1 (C++, C#, VB .NET, C++/CLI)
    • Microsoft Visual Studio 2010® (C++, C#, VB .NET, C++/CLI)
    • Microsoft Visual Studio 2012® (C++, C#, VB .NET, C++/CLI)
    • Microsoft Visual Studio 2013® (C++, C#, VB .NET, C++/CLI)
    • Microsoft Visual Studio 2015® (C++, C#, VB .NET, C++/CLI)
    • Microsoft Visual Studio 2017® (C++, C#, VB .NET, C++/CLI)
  • Since Open eVision 2.5.1, discontinued support of:
    • Borland C++ Builder 6.0 update 4 (C++)
    • CodeGear Delphi 2009 (Object Pascal)
    • CodeGear C++ Builder 2009 (C++)
    • Microsoft Visual Studio 6.0 SP6 (C++, Basic)
    • ActiveX API
  • Since Open eVision 2.4.1, discontinued support of:
    • Embarcadero RAD Studio XE4 and XE5 (C++, Object Pascal, 32 bits only)
Ordering Information
Product code - Description
Optional accessories
Presence Check

Presence / Absence check

EasyImage gray-scale analysis functions are used for simple presence/absence checks
Surface

Surface analysis

EasyImage is used to reveal the surface defects, and the blob analysis functions of EasyObject is able to segment and measure them.
Code Verification

Code quality verification for label printing machines