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Deep Learning Add-on


Deep Learning Add-on is a new breakthrough in machine vision applications. It is a set of ready-made tools which are trained with Good and Bad samples, and which then detect defects or features automatically. Internally it uses large neural network structures, designed and optimized by our research team for use in industrial inspection systems. For the user, however, they are provided as simple filters with very few parameters, and with easy-to-use graphical tools for convenient execution of the training process.

Deep Learning Add-on truly embodies the main principles of Adaptive Vision:

Intuitive - can be used even by users with no programming skills

Powerful - unleashing advanced capabilities of neural networks

Adaptable - deep learning models can be retrained to include new features.

Key Facts

Training Data

Typical applications require between 20 and 50 images for training.


A modern GPU is recommeded for fast training and execution.


Assuming use of a GPU, typical training time is 5 minutes and typical execution time is 200 ms.

Deep Learning vs Traditional Machine Vision

Deep Learning is a new reliable solution for machine vision problems that could not have been solved before. There are, however, applications that still can only be realized with traditional methods. How do you know, which approach is better? Here is a quick guide:

Deep Learning

Traditional Machine Vision

Typical applications:

    • Surface inspection (cracks, scratches)
    • Food, plant, wood inspection
    • Plastics, injection moulding
    • Textile inspection
    • Medical imaging

Typical applications:

    • Dimensional measurements
    • Code reading
    • Presence or absence checking
    • Robot guidance
    • Print inspection

Typical characteristics:

    • Deformable objects
    • Variable orientation
    • Customer provides vague specification with examples of Good and Bad parts
    • Reliability 99%

Typical characteristics:

    • Rigid objects
    • Fixed orientation
    • Customer provides formal specification with tolerances
    • Reliability 100%

Training Procedure

1. Collect and normalize images

    • Acquire between 20 and 50 images, both Good and Bad, representing all possible object variations; save them to disk
    • Make sure that the object scale, orientation and lighting are as consistent as possible
  • 2. Training

    • Use DeepLearning_ClassifyFeatures or DeepLearning_DetectAnomalies tools
    • Open Deep Learning editor
    • Load training images
    • Label images as Good or Bad (unsupervised mode), or mark defects with drawing tools (supervised mode)
    • Click “Train”
  • 3. Execute

    • Run the program and see the results
  • Training and Validation Sets

    In Deep Learning, as in all fields of machine learning, it is very important to follow correct methodology. The most important rule is to separate the Training set from the Validation set. The Training set is a set of samples used for creating a model. We cannot use it to measure the model’s performance, as this often generates results that are overoptimistic. Thus, we use separate data – the Validation set – to evaluate the model. Our Deep Learning tool automatically creates both sets from the samples provided by the user.

    Application Examples: Supervised Mode

    In the supervised mode the user needs to carefully label pixels corresponding to defects on the training images. The tool then learns to distinguish good and bad features by looking for their key characteristics.

    Photovoltaics Inspection

    In this application cracks and scratches must be detected on a surface that includes complicated features. With traditional methods, this requires complicated algorithms with dozens of parameters which must be adjusted for each type of solar panel. With Deep Learning, it is enough to train the system in the supervised mode, using just one tool.

    Satellite Image Segmentation

    Satellite images are difficult to analyse as they include a huge variety of features. Nevertheless, our Deep Learning Add-on can be trained to detect roads and buildings with very high reliability. Training may be performed using only one properly labeled image, and the results can be verified immediately. Add more samples to increase the robustness of the model.

    Textile Inspection

    Textile materials come in many different styles, but one thing is common – defects occur on a highly textured background. With Deep Learning technology, the user can define several classes of defects and mark them on sample images. When training is finished, classification is performed automatically, detecting even hardly visible defects.

    Cookie Inspection

    There are no two cookies that look the same, but customers expect one thing to stay perfect: the chocolate cover. How to define a defect? Simply collect faulty cookies and mark what is wrong with them. Our software learns the differences and reliably finds them on the product.

    Marble Cracks

    Detecting cracks on marble can be a challenging task, as by nature the material’s surface is not homogeneous. Our software is able to successfully distinguish between cracks and irregular patterns. All you have to do is to provide a couple of sample images, mark the cracks and train the model.

    Wood Knots

    One of the key factors taken into account while grading lumber is the number and size of knots. With Adaptive Vision's Deep Learning you can prepare a reliable application for knot detection and measurement in a matter of minutes. No programming skills required. Just add the samples and analyse the results with filters.

    Application Examples: Unsupervised Mode

    In the unsupervised mode training is simpler. There is no direct definition of a defect – the tool is trained with Good samples and then looks for deviations of any kind.

    Package Verification

    When a sushi box is delivered to a market, each of the elements must be correctly placed at a specific position. Defects are difficult to define when correct objects may also vary. The solution is to use unsupervised deep learning mode that detects any significant variations from what the tool has seen and learned in the training phase.

    Plastics, injection moulding

    Injection moulding is a complex process with many possible production problems. Plastic objects may also include some bending or other shape deviations that are acceptable for the customer. Our Deep Learning Add-on can learn all acceptable deviations from the provided samples and then detect anomalies of any type when running on the production line.