LabVIEW + AI Deep Learning Image Diagnosis Device

basic information

Expertise in Diagnosis ■ Positioning and Detection of Features Extracts single or multiple features from images and detects their positions. For example, it can detect and distinguish features even in cases of OCR with very noisy backgrounds or counting of complex, randomly piled parts. As long as a sample image with marked features to be extracted is prepared, learning is possible. ■ Detection of Defects and Anomalies Detects exceptions and visual defects. For example, scratches on cosmetic surfaces, assembly defects in devices, or weaving patterns in textiles. By simply learning the appearance of objects, it is possible to distinguish many other issues as well. Additionally, it separates specific areas such as defects from other areas. For instance, foreign substances in medical non-woven fabrics or the mesh of lace. All these targets are learned simply based on differences in appearance. ■ Classification of Targets Classifies targets. For example, sorting products, classifying welding seams, and distinguishing between acceptable and unacceptable defects. Learning is based on a collection of labeled images, enabling classification. As long as labeled images that correspond to each class are prepared, learning can be conducted.

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Applications/Examples of results

- Appearance inspection of machined parts - Inspection of mounted circuit boards - Inspection of printing and printing misalignment - Detection of the position of appearance features and counting of quantities - Detection of surface defects - Color pattern and design inspection - Product sorting based on appearance