Mariner

Hardware : Sensor Systems : Machine Vision

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Charlotte, North Carolina, United States

Mariner provides manufacturers with technology solutions that leverage IoT, AI and deep learning to improve operational effectiveness, increase quality and gain competitive advantage. We jump start and sustain manufacturers’ Industry 4.0 journey.

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Why AI software companies are betting on small data to spot manufacturing defects

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Author: Kate Kaye

Topics: Machine Learning, Visual Inspection, Defect Detection

Organizations: Landing AI, Mariner

The deep-learning algorithms that have come to dominate many of the technologies consumers and businesspeople interact with today are trained and improved by ingesting huge quantities of data. But because product defects show up so rarely, most manufacturers don’t have millions, thousands or even hundreds of examples of a particular type of flaw they need to watch out for. In some cases, they might only have 20 or 30 photos of a windshield chip or small pipe fracture, for example.

Because labeling inconsistencies can trip up deep-learning models, Landing AI aims to alleviate the confusion. The company’s software has features that help isolate inconsistencies and assist teams of inspectors in coming to agreement on taxonomy. “The inconsistencies in labels are pervasive,” said Ng. “A lot of these problems are fundamentally ambiguous.”

Read more at Protocol

Mariner Speeds Up Manufacturing Workflows With AI-Based Visual Inspection

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Author: Angie Lee

Topics: computer vision, defect detection

Organizations: Mariner, NVIDIA

Traditional machine vision systems installed in factories have difficulty discerning between true defects — like a stain in fabric or a chip in glass — and false positives, like lint or a water droplet that can be easily wiped away.

Spyglass Visual Inspection, or SVI, helps manufacturers detect the defects they couldn’t see before. SVI uses AI software and NVIDIA hardware connected to camera systems that provide real-time inspection of pieces on production lines, identify potential issues and determine whether they are true material defects — in just a millisecond.

Read more at NVIDIA Blog