MVTec Software GmbH
 

Deep Learning

MVTec software products offer a large selection of operators, functions and methods that are either based on deep learning technologies, or allow customers to use deep learning technologies in their own applications.

Deep-Learning-Powered Technologies

Featured in HALCON and MERLIC

HALCON (free trial here) and MERLIC (free trial here) both include deep-learning-based OCR (check out this video to see it in action). Training of these new classifiers has been conducted with the help of deep learning technology. With these it is possible to achieve higher reading rates than with all previous classification methods.

Deep Learning in Your Application

Featured in HALCON

HALCON allows customers to use deep learning technologies in their own applications. Users can train their own classfier using CNNs (Convolutional Neural Networks). These can then be utilized to classify new data.

Training user-specific CNNs

training the network by labeling images
CNN training with labeled image data

With MVTec HALCON, users can train their own classifier using CNNs (Convolutional Neural Network). After training the CNN, it can be used for classifying new data.

Training is done simply by providing a sufficient amount of labeled training images. E.g., to be able to differentiate between samples that show scratches or contamination and good samples, training images for all three classes must be provided: Images showing scratches must be labeled "scratch", images showing some sort of contamination must carry the label "contamination", and images showing a good sample must be in the category "OK".

The software then analyzes these images and automatically learns which features can be used to identify defective and good samples. This is a big advantage compared to all previous classification methods, where these features had to be "handcrafted" by the user – a complex and cumbersome undertaking that requires skilled engineers with programming and vision knowledge. It is the first time that customers can train CNNs with HALCON on the basis of deep learning algorithms, using sample pictures of their specific application. Thus, the resulting network can be highly optimized to the customers’ needs.

Using the trained networks

Defect classification with deep learning
Trained CNN classifying defects

Once the network has learned to differentiate between the given classes, e.g., tell if an image shows a scratched, a contaminated or a good sample, the network can be put to work. This means, users can then apply the newly created CNN classifier to new image data which the classifier then matches to the classes it has learned during training – this is also called "inference".This inference can be executed on GPUs, as well as on CPUs.

Application Areas

When looking for real-world applications, CNNs can for example be used for defect classification (e.g., for circuit boards, bottle mouths or pills), or for object classification (e.g., identifying the species of a plant from one single image).

Developed with Key Customers from Various Industries

While developing HALCON's deep learning features, we worked closely together with key pilot customers from various industries. Below, you find a quick overview of the different challenges and how deep learning helped us solve them.

Plant identification

The task: Identify plants

Before deep learning

New plant types had to be manually programmed. Separate MLP classifiers were used for feature extraction for each country – a time-consuming process.

With deep learning

It took us only around two weeks to solve the same tasks The error rate could be halved.

Identify emtpy rack spaces with deep learning

The task: identify empty rack spaces

Before deep learning

An elaborate hardware setup with 3D sensors was needed.

With deep learning

The solution works with 2D image data which - thanks to cheaper image capturing and quicker classification - translated into cost savings. In addition, the error rate could be significantly reduced.

Mood image: detect defective pills with deep learning

The task: detect defects on pills

Before deep learning

New defect classes had to be programmed manually and on site – a time-consuming and costly process.

With deep learning

New defect types can be trained automatically, a huge time- and cost-saver because engineers no longer have to travel to the customer's plant.

Lands inspection with deep learning

The task: check lands for defects

Before deep learning

A relatively high error rate required a lot of manual inspection by workers, slowing down production and increasing costs.

With deep learning

Implementing a deep-learning-based defect detection led to a massive reduction of the error rate, which drastically reduced the need for manual inspection, speeding up production and scaling down labor costs.

Deep Learning Tutorials

Our three-part tutorial series leads you through all necessary steps required to train and evaluate your own deep-learning-based classifier with HALCON.

Image preprocessing
(1) Image preprocessing
Training the classifier
(2) Training the classifier
Evaluate a trained classifier
(3) Evaluate a trained classifier