Artificial Intelligence (AI)

Deep learning that automates factory defect

Defect Inspection Factory Automation

Conventional Approach

When quality control fails the consequences can be damaging.
Potential fallout can include high-profile recalls, dissatisfied customers, customer loss and a tarnished reputation.
Automated imaging-based analysis using machine vision systems offer manufacturers a certain level of assurance in defect inspection as do manual reviews by trained and qualified inspectors. Yet these safeguards can be inconsistent with some anomalies too difficult to catch making quality failure possible.

Simplified Oversight

NEC RAPID Machine Learning works by creating an inspection model based on training image data that defines the quality parameters of an object.
Building a repository of potential defect types is easy with NEC’s exclusive marking detection tool
that allows customers to inspect defects automatically.

All it takes is three simple steps with the tool’s user interface which is intuitive to use and require little training to master.

Step 1
Load good quality
and defective product images.

Step 2
Mark defect areas.

Step 3
Feed collected data.

Feed data will be used by NEC RAPID Machine Learning to create its defect inspection engine which can be utilized
by users to inspect defects automatically.

What Sets NEC Apart

30+ Years Of Experience In Machine Learning/Deep Learning Research


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