LINX performs deep learning applications with MVTec HALCON

Electric Components | HALCON | Classification | Deep Learning | Object Detection

LINX is the premier distributor for machine vision and automation products in the Japanese market. As part of the LINXDays in Japan, they built a demo setup to showcase HALCON’s deep-learning-based object detection. You can download their example program here.

Components used:

  • OS: Windows 10, 64bit
  • HALCON version: 18.11
  • CPU: Intel Core i7-8700


  • Training Data: About 100 objects/images for each class
  • Training Time: About 3 hours
  • Inference Time (GPU): About 25 ms for each image


The application detects and recognizes electric components (capacitors, transistors and integrated circuits), a useful preprocessing step for, e.g., automatic optical inspection (AOI) and surface-mount technology (SMT). It is difficult to detect the location of these components by using traditional, rule-based pattern matching technology, due to several types of shapes per category, as well as differing appearances depending on the position on the image. Consequently, using a deep-learning-based detection approach proves to be an efficient solution for this use case.

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