MVTec’s lastest Halcon release includes an extensive set of cutting-edge deep learning functions, that can be readily applied to industrial inspection tasks.
It is the first time that customers can train their own CNNs (Convolutional Neural Networks) with MVTec HALCON on the basis of deep learning algorithms, using sample pictures of their specific application. Training a CNN in HALCON is done simply by providing a sufficient amount of labeled training images. E.g., to be able to differentiate between sample images that show defects/contamination and good image samples. Image data can now be classified easily and precisely, thus reducing programming effort and saving both time and money. The new deep learning feature is fully integrated into the HALCON machine vision library.
In addition to the mentioned deep learning technologies, HALCON 17.12 offers many other new features, product improvements, and revised functions. For example, the software enables the users to inspect specular and partially specular surfaces to detect defects by applying the principle of deflectometry. This method uses known pattern projections that are reflected by the specular surface and the software observes changes in that pattern for inspections purposes.
The new release also simplifies the daily work of HALCON developers, since it is now much easier to integrate the HDevEngine into the developer’s application. Another highlight is the improved automatic text reader, which has become even better at detecting and reading letters and numbers that are touching.
Finally, HALCON 17.12 offers a new method to fuse the data from different 3D point clouds into one unified model. This new method is able to combine data from various 3D sensors, even from different types like a stereo camera, a time of flight camera, and fringe projection. This 3D-based, cutting-edge technology simplifies
the post-processing of such point clouds through a much more accurate reconstruction of objects. This is important for processes such as reverse engineering. For example, if there is no CAD model available, or if objects must be analysed more precisely regarding their 3D properties.