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Deep Learning

Challenges to Machine Vision

Rules-based algorithm

Rule-based algorithms struggle to program complex inspections involving deviation and unpredictable defects

Traditional machine vision systems perform reliably with consistent, well-manufactured parts. They operate via step-by-step filtering and rule-based algorithms that are more cost-effective than human inspection. But algorithms become challenging to program as exceptions and defect libraries grow.

Machine vision systems tolerate some variability in a part’s appearance due to:
  • Scale
  • Rotation
  • Pose distortion

Nonetheless, complex surface textures and image quality issues introduce serious inspection challenges. Machine vision systems struggle to appreciate variability and deviation between very visually similar parts. “Functional” anomalies, which affect a part’s utility, are almost always cause for rejection, while cosmetic anomalies may not be, depending upon the manufacturer’s needs and preference. Most problematically, these defects are difficult for a traditional machine vision system to distinguish between.

Car Trim Wire Assembly 

Certain traditional machine vision inspections, such as final assembly verification, are notoriously difficult to program due to multiple variables that can be hard for a machine to isolate such as:
  • Lighting
  • Changes in color
  • Curvature
  • Field of view

For complex inspections involving deviation and unpredictable defects, which can be too complicated to program and maintain, deep learning-based software offers an excellent alternative.

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