HALCON 20.05 Feature-Preview: Further Improvements

Grad-CAM-based Heatmap in HALCON 20.05

CPU Support for Grad-CAM-based Heatmap

The Grad-CAM-based heatmap (Gradient-weighted Class Activation Mapping) supports you in analyzing which parts of an image influence the classification decision. In HALCON 20.05, the heatmap calculation can also be performed on the CPU. Since this can be done without significant speed drops, customers are now able to analyze their deep learning network's class prediction "on the fly".

More robust Generic Box Finder

The generic box finder, which was released with HALCON 19.11, allows users to locate boxes of different sizes within a predefined range of height, width, and depth, removing the need to train a model. With HALCON 20.05, it was improved in terms of robustness, performance, speed, and usability. Now, it is much easier to find a wide range of different sizes of various boxes in a robust way.

Anomaly Detection Improvements

The anomaly detection significantly facilitates the automated surface inspection by only requiring a low number of high quality "good" images for training.

With HALCON 20.05, training a network for anomaly detection is now up to 10 times faster. Combined with an also faster inference, this opens up entirely new possibilities for trying deep learning on new and existing applications: Training a new network can now mostly be done in a matter of seconds, allowing users to perform many iterations to fine-tune their application without sacrificing a lot of precious time.

Trained networks now also require less memory and disk space, which makes HALCON's anomaly detection more viable for the use on embedded devices.