Deep Learning with Neuralyze®

Neuralyze® by senswork

senswork has developed the deep learning software Neuralyze® for quality tests with complex test objects. The efficient inspection software is suitable for tasks that cannot be solved with conventional image processing. This includes, for example, inspections of test objects with transparent, reflective, curved or inhomogeneous surfaces or the detection of products with a high variance of features.

A self-learning method with neural networks is implemented to assess characteristics. A large amount of image data is required for the training process, with the help of which the algorithm is then optimized.

With Neuralyze, for example, cracks, scratches, voids, fibers, grooves, hair, oxidation, delamination or inclusions can be detected.

 

Deep Learning with Cognex ViDi Suite

Your Contact Person

If you have any questions or are interested in consulting, please feel free contact me.

Markus Schatzl senswork
Markus Schatzl

+49 (0)89 215 298 46 0
markus.schatzl@senswork.com

senswork GmbH
Innovation Lab
Friedenstraße 18
81671 München

Projects

Surface Inspection of Cylindrical Aluminum Bodies
Examination of surface damage through 360° endoscopic inspection
Praline Box Inspection
Location Detection and Completeness Confirmation using Deep Learning
Quality Assurance for First Aid Kits
Location Detection and Completeness Confirmation using Deep Learning
Reading a Raised Inscription on Smartphone Cases
Robust Reading of the Label using Deep Learning based OCR
Reading the „best before“ date on beverage bottles
Font Reading for Transparent Packaging using Deep Learning
Reading the „best before“ date on bottoms of bottles
Font Reading for Transparent Packaging using Deep Learning
Error Detection for Tortillas
Optical Inspection using Deep Learning
Quality assurance for LED curcuit boards
Error detection with the help of Deep Learning
Inspection of Glass Vials
Defect Detection on Reflective Surfaces using Deep Learning
PCB assembly verification of smartphone boards
Component Placement Inspection using Deep Learning
Spark Plug Inspection
Location Detection and Completeness Confirmation using Deep Learning
Air Filter Fault Detection
Examination of Surface Damage using Deep Learning
Recognition of Fonts for Ring Clamps
Optical Inspection of Surfaces with Inhomogeneous Topology
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