Deep Learning Image Analysis in Factory Automation · OCR-A on soft, semi-transparent plastic...
Transcript of Deep Learning Image Analysis in Factory Automation · OCR-A on soft, semi-transparent plastic...
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Deep Learning ImageAnalysis in Factory Automation
Real-World Applicationsin Production
Olivier DespontCognex Corporation
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Example-Based Approach
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Adding value –
Best applications for Deep Learning in Machine Vision
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When Applying Deep Learning in Machine Vision ?
Self-LearningExample-based training Human-like decisions
Watch dials inspection Cement spots inspection
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When Applying Deep Learning in Machine Vision ?
Finds distorted orinconsistent parts
Finds surface defects
Automated bone cutting Rail inspection
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Precision Alignment Gauging
Plastic part location Gap check
When Deep Learning is Not Suitable?
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What To Look For in a Production Deep Learning System?
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Works on commercial PCs
Handles camera mounting effects
Works with limited data sets
Works with high res, color, thermal and 3D images
Image
Informatio
n
Confusio
n Matri
x
Score
Plots
Doesn’t require Ph.D. to configure
∆ η η∑ target - output )( )
What To Look For in a Production Deep Learning System?
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Cosmetic InspectionSurface Inspection- Cogwheel Automotive
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Cosmetic InspectionSurface Inspection- Cylindrical Motor Mechanism
Rust Spot (OK)
No anomalies (OK) White Area (OK)
Broken (OK)
Defects
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Cosmetic InspectionSurface Inspection- Cylindrical Motor Mechanism
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and you can also apply this technology
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Part LocationDeformable part location and counting
Medical vials counting on a tray
2 images to teach30 min to built the application (incl. training)
Works despite translucent and touching glass vials on shiny metal conveyor with circular background.
Also handles perspective variation due to wide angle lens
Processing time with a GTX 1080 : 80ms/imageImage size : 1800x1450Identification rate : 98%
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Bone removal
Part LocationPath following for robot
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Pre-insertion screw & debris check
Pre-Assembly VerificationPre-Assembly Obstruction Check
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IV bag orientation
Pre-Assembly VerificationPart Correctness and Orientation
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Pre-Assembly VerificationKitting & Palletization
Automotive door handles
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ClassificationBulk & Batch Product Identification
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Appearance-based diary product identification for logistic application
Image classification with many classesAnd large within class variation
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Welding seams overlap (OK)
Correct Welding Seams (OK) Welding seams overlap (OK) Defective Welding Seams (NOK)
Welding seams defect (NOK)
Images courtesy byLEONI Engineering Products & Services, Inc., Lake Orion, MI - USA
ClassificationDefect Classification
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ClassificationDefect Classification on Welding Seams
Classify Anomalies based on the results of a region extractor
Images courtesy byLEONI Engineering Products & Services, Inc., Lake Orion, MI - USA
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Stamped characters on metal ingotsOCR-A on soft, semi-transparent plastic pouches
Inkjet printed text on bottom of aluminum cans
Characters placed closely to barcode
Molded characters in plastic parts
OCR on non-flat surface with varying light conditions
Pad-printed characters on gas tanksDeformed OCR
Hard-to-Read CharacterDifferent OCR Applications
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Post-Placement Packaging Check
Surgical Kit completeness
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Post-Placement Packaging Check
Swiss chocolates
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What Kind of Applications in Machine Vision Can Deep Learning Solve?
Cosmetic Inspection• Surface Inspection• Functional defect detection
Part Location• Deformable part location and
counting• Path following for robot
Pre-Assembly Verification• Pre-assembly clearance check• Part Correctness and Orientation• Kitting & Palletizing
Classification• Bulk/batch identification• Defect classification
Hard-to-Solve OCR• Distorted character
detection
Post Assembly• Placement check• Final assembly &
packaging verification
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human performanc
e and flexibility
Reliabiltyand
consistency
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Towards a truly “Smart” Camera …?
What could be the future?
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Olivier Despont Product Marketing SpecialistCognex Corp – Switzerland
Office +41 26 653 72 78
Contact Information