Auto Deep Learning Vision Inspection

The new GAN Patch mode at Neurocle's Neuro-T deep learning software slices images into patches to generate more precise synthetic defect images. – Image: Neurocle Inc.

Patch Classification (PAC): One feature is the introduction of the PAC model, designed to overcome the limitations of traditional classification models that struggle with high-resolution image data. Previously, resizing high-resolution images for analysis often led to a loss in detecting defects. This model segments high-resolution images into smaller patches, ensuring that even the tiniest defects are accurately detected without compromising on resolution.

GAN Patch Mode: For situations where defect data is scarce, Neurocle has enhanced its Defect Generator (GAN) model capabilities. The new GAN Patch mode slices images into patches to generate more precise synthetic defect images. This advancement facilitates the adoption of deep learning inspection in manufacturing environments with limited initial defect data, reducing the burden of data collection and accelerating the implementation of deep learning-based inspection systems.

Shape Converter and Auto-Labeling Tools: The Shape Converter and Auto-Labeling tools provide solutions for meticulous labeling tasks. The converter transforms box labeling areas into detailed polygon shapes with a single click, while the labeling tool applies consistent labeling standards across all images after labeling just a few samples.

Expanded Processor Support: With support for Openvino, DirectML, GPU, CPU, NPU, and embedded boards, the software is capable of integrating into various manufacturing inspection setups.

Hall 8 | Booth B11

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