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 our example program here.
Components used:
- OS: Windows 10, 64bit
- HALCON version: 18.11
- CPU: Intel Core i7-8700
- GPU: NVIDIA GTX1080Ti
Training:
- Training Data: About 100 objects/images for each class
- Training Time: About 3 hours
- Inference Time (GPU): About 25 ms for each image
Application:
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.
