How to use Cognex VisionPro ViDi to solve difficult

© 2019 Cognex Confidential1

How to use Cognex VisionPro ViDi to solve difficultapplications on the Factory floor?

Olivier Despont – Field Product Marketing Manager | July 4th 2019


WHAT IS COGNEX DEEP LEARNING TECHNOLOGY?

human

performance

and flexibility

Reliability and

consistency


WHAT IS COGNEX DEEP LEARNING TECHNOLOGY?

Different approach to solving vision problems

▪ Example-based training modeled on human learning

▪ Not a rigid rules-based solution


How it works - Active Perception

CNN


END-TO-END LEARNING & BLACK BOX RESULTS

Traditional Deep Learning

approaches

Cognex ViDi approach

scratch

dent

stain

dust


COGNEX DEEP LEARNING FOR FACTORY AUTOMATION

Works on a commercial IPC

with high resolution images

Works with limited data sets

Bundled with VisionPro™ for the best of Deep

Learning and traditional visionDoesn’t need a Deep Learning Ph.D. to

configure & maintain

Image

Informat

ion

Confusi

on

Matrix

Score Plots

∆𝒘𝒋 =

−𝜼𝝏𝑱

𝝏𝒘𝒋= 𝜼σ𝒊(𝒕𝒂𝒓𝒈𝒆𝒕

(𝒊) - 𝒐𝒖𝒕𝒑𝒖𝒕(𝒊))(𝒙𝒋(𝒊)

)


WHY VIDI?VS. TRADITIONAL MACHINE VISION

Human-like judgment Handles variable part appearance and unpredictable defects

Train by exampleField maintainable


WHEN SHOULD WE USE DEEP LEARNING VISION?

ViDi Solves problems traditional vision cannot

Inspection

Deformed characterOCR

ViDi for unpredictable features

Distorted part location

VisionPro™ classic vision is best for

Precision alignment

1D & 2D code reading

VisionPro for consistent features

Gauging


WHY VIDIVS. MANUAL INSPECTION

Faster▪ Keep pace with in-line production

Consistent results▪ Every shift, every line, every factory

Continuous improvement▪ Additional examples after initial deployment

Documented results

▪ Yield improvement via real-time process control

▪ Analytics data for offline improvement initiatives

▪ External reporting to end customer

▪ Traceability, for future failure analysis


SolveComplex Inspection Human-like algorithm allows to tackle formerly hard-to-solve application by tolerating natural parts variation while focusing on what matters

ReduceMaintenance costEasy set-up and powerful algorithm configurable on Factory floor by operator

AT A GLANCE – BENEFIT OF VISIONPRO VIDI

Speed up DevelopmentNo programming thanks to easy set-up and powerful algorithm configurable through Intuitive GUI

Train & ProcessImages faster Powerful and Optimized Algorithms need low number of images and processed on single GPU

BenefitBest-in-class software & worldwidesupportBest Software for image analysis in Factory automation and supported by Cognex High-skilled engineers

ImproveQuality & Defects traceabilityConsistent 24/7 documented inspection results

IncreaseProduction YieldAutomatization of human low value and low performing tasks

6 -12 Months ROIPayback when deploying ViDi in the factory


What applications can ViDi solve?


Cosmetic InspectionSurface Inspection

Functional defect detection

Part locationDeformable part location and counting

Path following for robot

Pre-Assembly verificationPre-assembly clearance check

Part Correctness and Orientation

Kitting & Palletizing

ClassificationBulk/batch identification

Defect classification

Hard-to-solve OCRDistorted character detection

Post AssemblyPlacement check

Final assembly & packaging

verification

WHAT APPLICATIONS CAN VIDI SOLVE?


Applications

Optimized tool for assembly verification

Confirm that all parts are present & correct

Share components across many layouts

For Automotive, Medical, Consumer Electronics, Food, …

BenefitsHandle 3D and deformed parts

Re-use component models without re-training

Optimized tool for OCR

Out-of-the-box reading

Retraining capabilities to be more application specific

Benefits

Facilitate image labelling

Read hard-to-tackle applications

Optimized tool for well-defined cosmetic inspection

Unsupervised/supervised inspection

Defects classification

Benefits

Train on few Good images

Handles variations without programing


Automotive Fusebox Assembly

Wrong ComponentPass


Electronics Assembly

Pass Missing Screw


Food Packaging

Pass Missing Salami Wrong Placement


Assembly Check tool demo


Pre-closing verification and qualityinspection

Presence / absence and counting Cosmetic Inspection


Metal Parts OCR

Trained on 183 images

Validation than 1000+ images

Results: > 99.5%

Time Cycle : 3 months

OCR examples

Generalized OCR for non-flat poorly inkjet printed label package

Trained on ca. 1000 images (99.5%)

Results after deployment: > 99.8%

Time Cycle : 5 months

Reading station images fed by DataMan fixed-mount readers


Rotated Characters

▪ Added ability to read rotated characters (curved strings)

▪ Better out-of-the-box performance

▪ Easier and faster to develop OCR solution

Read Tool Enhancements with 3.4

ViDi 3.4

ViDi 3.3


Well-defined Cosmetic Inspection Success Stories – Weld beads

Large variability across good parts

Large variability across bad parts but obvious differences compared to good parts


Feasibility on 100s images

Applications building on 6535 images and Training the applications on 50% good (2969 images)

Fine tuning and re-training on reference set #1 Validation/Deployment/Optimization on 32,901 images

100% defect detection

99.82% good detection

(0.18% good parts consider as bad)

Well-defined Cosmetic Inspection Success Stories - Weld beads

Description Quality % Good as good 32,240 99.82%

Good as intermediate 20 0.06%

Good as bad 38 0.12%

Total good images 32,298

Bad as good 0 0.00%

Bad as intermediate 2 0.33%

Bad as bad 601 99.67%

Total bad images 603

Total images 32,901

Description Quantity %

Good as good 5937 99.82%

Good as intermediate 3 0.05%

Good as bad 8 0.13%

Total good images 5,948

Bad as good 0 0.00%

Bad as intermediate 1 0.17%

Bad as bad 586 99.83%

Total bad images 587

Total images 6,535


Full comprehensive range of machine vision software platform allowing to combine best of both world to solve MV applications

What makes ViDi different from the competition – 8 reasons to convince

Easy to use interface helping

to reduce development time

Unsupervised Anomaly Detection

permitting to teach on good

images only

Most Powerful OCR and easy to

set up to solve complex

character reading applications

Chainable tools in the same

workspace to facilitate the

development of application

and speed up time to market

Pretrained font OCR to

reduce time to label

images

3x-5x Faster in training and

processing images

No programming required

allowing building

complex application

quickly


How to start with Deep Learning ? Cognex can assist & support you


A well-designed pilot project is key▪ Not too easy & Not too hard

▪ Constrain the application (Prioritization)

Clear understanding ▪ Acceptance criteria (well-defined, image-based, globally

agreed)

▪ Current process (Material handling, Inspection decisions,

Manufacturing yield, rework, throughput)

▪ Resources, schedule and budget

Integration in Production ▪ In-line Inspection (Simple step, high speed, camera

available)

▪ Final Inspection (Metrics exists, ROI, complex tasks, high

value station)

What makes a good first project ?


Team : 4 core roles required ▪ Vision Developer (Implemented SW and build/optimize the

application)

▪ Quality Expert (Analyzes images to determine correct “classification” and determine Acceptance Criteria)

▪ Image Labeler (Marks defects and training features on the image database – CONSISTENCY)

▪ Data Collector (Record projects test and production data (images, results,…) and organize them (for debugging, yield calculation, validation)

Schedule :▪ Allocate sufficient time for factory-based

development (Image acquisition / Golden database definition may require several phases (system tested & product variations identified)

▪ Allow for AI learning curve (it is a NEW approach)

Statistically valid data sets are the only way to validate Deep Learning in production

Development Resources & Schedule


Project phases

Prototyping

Understand the current process and determine if ViDi is a good candidate to solve it

Image Data Collection

Integrate the system on the production line and begin gathering and organizing data

Optimization (Lengthiest step)

Improve the ViDi solution until it meets the performance target

Validation & DeploymentQualify the solution and begin using it in production

Determine application requirements & prioritize.

Acquire a small database of graded & labeled

images.

Build a proof of concept system to test the

approach.

Integrate the camera and lighting on the

production line.

Begin logging image data and manual

inspection results (if any)

Establish ground truth data, optimize and

labelled image sets (CONSISTENTLY)

Run ViDi against your image data sets and in

production

Compare the results to ground truth and to

manual inspection results (if any)

Adjust the system and re-train as needed

(steady improvement over time)

Pass factory acceptance tests and lock

configuration (statistically proven)

Integrate into production and expand to

additional lines, prepare for future changes

Establish continuous monitoring process

Tasks / Actions


Traditional

Machine VisionDeep Learning

Documents

How to use Cognex VisionPro ViDi to solve difficult