Deep Learning is a new reliable solution for machine vision problems that could not have been solved before. There are, however, applications that still can only be realized with traditional methods. How do you know, which approach is better? Here is a quick guide:
Deep Learning
Traditional Machine Vision
Typical applications:
Surface inspection (cracks, scratches)
Food, plant, wood inspection
Plastics, injection moulding
Textile inspection
Medical imaging
Typical applications:
Dimensional measurements
Code reading
Presence or absence checking
Robot guidance
Print inspection
Typical characteristics:
Deformable objects
Variable orientation
Customer provides vague specification with examples of Good and Bad parts
Reliability 99%
Typical characteristics:
Rigid objects
Fixed orientation
Customer provides formal specification with tolerances