Teledyne announces AI enhancements to its Sapera Vision Software

Publisher:EE小广播Latest update time:2022-05-20 Source: EEWORLDKeywords:Teledyne Reading articles on mobile phones Scan QR code
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Astrocyte uses stitched images (2800x1024) to locate and identify small nodules on a wooden board (30x10)


WATERLOO, Canada, May 19, 2022 – Teledyne is pleased to announce the immediate availability of its Sapera Vision Software 2022-05 release. Teledyne DALSA’s Sapera Vision Software provides field-proven image acquisition, control, image processing, and artificial intelligence capabilities to design, develop, and deploy high-performance machine vision applications. The new upgrade includes enhancements to its AI training graphics tool Astrocyte and its image processing and AI library tool Sapera Processing.



“We are excited to introduce new capabilities to our Astrocyte and Sapera Processing packages. With the new stitching capabilities, users can detect the smallest defects on large images at native resolution,” said Brandon Hunt, Product Manager, Vision Solutions Group at Teledyne. “In the latest updates, we have included improved anomaly detection algorithms, live video acquisition from frame grabbers, and new features that provide higher performance and a better user experience. Sapera Processing 9.30 has improved AI and 3D tools,” he continued.


Sapera Vision Software is ideal for applications such as sheet metal surface inspection, hardware component location and identification, vehicle detection and segmentation, and X-ray medical image noise reduction.


New features in this version:


Large Image Stitching – A mechanism to process large images at their native resolution to avoid loss of precision on very small defects/objects.


Improved performance of anomaly detection – A new anomaly detection algorithm that performs better at low and medium resolutions.


YOLOX Object Detection – An additional object detection algorithm with higher performance and a smaller footprint.


Non-square image handling - a flexible mechanism to prepare images for training and inference without distorting the original aspect ratio.


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