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FZ-S/SC bảng dữ liệu(PDF) 5 Page - Omron Electronics LLC |
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FZ-S/SC bảng dữ liệu(HTML) 5 Page - Omron Electronics LLC |
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5 / 54 page ![]() Test is automatically performed using images prepared in Step 1. You don’t need to adjust parameters for differential inspections. When a good product is judged as defective, AI gives each image a correlation score to visualize the degree of overdetection. This facilitates selecting images that need to be learned to reduce overdetection. 5 Prepare images 1 2 3 Create model Check results Good product image Defective product image AI makes it easy to avoid overdetection Three quick steps on the settings screen guide the user through the process of creating the good product model with the minimum number of images. Image A:10 Image B:150 Correlation score AI model AI Fine Matching identifies a future that is not included in good products as a defect. AI learns images of good products with variations, and generates an AI model. Every time an inspection is carried out, AI reconstructs a model that is presumed to be a good product. AI extracts a difference between the reconstructed good product image and a captured image to identify a defect, reducing overdetection. Although standard AI processing requires a huge number of images for learning, the FH Series requires only 100 to 200 images. The system suggests images to learn, helping to complete the good product model. *1. “Patent pending” means that we applied for a patent in Japan, and “Patented” means that we obtained a patent in Japan. (as of May 2022) Shifted to upper right Shifted to lower left Reconstructs a good product image considering different views of holes Captured image AI model Reconstructed good product image *1 PATENT PENDING AI makes it easy AI makes it easy AI reduces overdetection AI Fine Matching |
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