Examples
Standard Software using COGNEX VisionPro DeepLearning
| Category | Automotive components |
|---|---|
| Workpiece | Engine |
| Inspection details | Foreign matter detection |
| Application | Deep Learning + Rule-Based |
Before
It is a captured image of the Engine parts.
The packing is attached to a metal plate, but the position is slightly different each time, so it is difficult to detect the misalignment because it is detected as a defect in the existing image processing.
The packing is attached to a metal plate, but the position is slightly different each time, so it is difficult to detect the misalignment because it is detected as a defect in the existing image processing.
After
By using deep learning, it is possible to detect defective parts by teaching typical defective parts.
[Main characteristics]
Inspection is possible by combining Rule-based image processing and Deep Learning.
With this software, you can easily realize processing such as image filter, alignment, image cropping processing by Rule-based image processing,
and then defect inspection by Deep Learning.
Furthermore, it is also possible to measure the area based on the rules for the inspection results of Deep Learning.
Rule-based image processing ・・・ VisionPro
Deep Learning image processing ・・・ VisionPro Deep Learning
Suitable for the following inspections.
・ Defect detection
・ Product Classification
・ optical character recognition
・ Assembly inspection
Inspection is possible by combining Rule-based image processing and Deep Learning.
With this software, you can easily realize processing such as image filter, alignment, image cropping processing by Rule-based image processing,
and then defect inspection by Deep Learning.
Furthermore, it is also possible to measure the area based on the rules for the inspection results of Deep Learning.
Rule-based image processing ・・・ VisionPro
Deep Learning image processing ・・・ VisionPro Deep Learning
Suitable for the following inspections.
・ Defect detection
・ Product Classification
・ optical character recognition
・ Assembly inspection