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DMC NI Week 2014 High Speed Vision
The document outlines a presentation on high-speed vision inspection techniques, led by DMC Inc. presenters Kenneth Brey and Eric West, discussing various hardware architectures, customer-focused case studies, and algorithmic strategies for real-time image processing. Key topics include definitions of high-speed vision, hardware options like windows PC-based solutions and smart cameras, and rapid algorithm tips that enhance processing efficiency. The emphasis is on delivering high-speed inspection solutions across multiple industries, ensuring zero defects, and meeting customer demands for complexity and speed.
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DMC NI Week 2014 High Speed Vision
- 1. 1ni.com Real-world Techniques in High-SpeedVision Inspection Wed 4:45 – 5:45 Room 17b
- 2. 2ni.com Presenters • Kenneth Brey– P.E. • Technical Director, DMC Inc. • Eric West • Project Engineer, DMC Inc.
- 3. 3ni.com Agenda 1. DMC companyoverview 2. High Speed Vision - Definition 3. Customer Focus – W.H. Leary 4. Hardware Architectures 5. Camera triggering with FPGA 6. Parallelism 7. Rapid algorithm tips 8. Product Training 9. Q&A Along the way: • Case Studies • Knowledge Nuggets
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- 5. 5ni.com DMC Company Profile IndustriesServed: Automotive Chemical and Food Processing Electronics/Semiconductor Hydraulics Laboratory Testing Machine Tool Material Handling Metal Converting Packaging Pharmaceutical Printing & Textiles Established in 1996, offices in Chicago, Boston & Denver & customers throughout the world employees & growing 70+
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- 7. ni.com High Speed MachineVision Definition: • Automatically processing many images per second and acting on the result in real time. Is different than High Speed Photography, which is about acquiring many images per second.
- 8. ni.com Customer Focus Daniel McCarty Sr.Software Development Mgr. W. H. Leary Co., Inc. Tinley Park, IL
- 9. 9ni.com Customer Scenario –Global Consumer Packaging Solutions Challenges: 1. Factory lines running products over 200,000 pieces/hr 2. Microseconds to decide whether a product is good 3. Self-monitoring, self-compensating systems 4. Track and reject bad product
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- 12. 12ni.com TraditionalApproaches: In-line Sensing,I/O • Photoeye, Glue valve (“gun”), and Sensor • 4, 8, 16, or 24 of these groups (stations) • What about out-of-line defects? • Glue sling, Carton scrap, Crossing glue lines • Complex packaging requires 30+ stations
- 13. 13ni.com High Speed ComplexCartons
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- 15. 15ni.com HardwareArchitectures - Overview Compare: 1.Windows PC Based Solutions 2. Smart Cameras 3. LabVIEW RT Based Solutions More info from NI at: http://www.ni.com/webcast/1063/en/
- 16. 16ni.com HardwareArchitectures – WindowsPC Based Separate Imagers, Windows PC • Advantages: • Buy Processor / Software Licenses Once • Integrated HMI • Works with standard cameras, plus odd-balls • Easy image storage • Leading edge processor performance • Disadvantages: • Not deterministic (subject to Windows “naps”) • I/O and image acquisition loosely integrated* *Exceptions include NI FPGA-Enabled Vision RIO cards. C C C
- 17. 17ni.com HardwareArchitectures – SmartCameras Bundled Solution: Imager, Processor, Software, I/O • Advantages: • Great communication to PLCs • Distributed Model: Scalable Performance • Deterministic performance. (no naps) • Disadvantages: • Buy Processor/Software with Each Camera • Multi-camera inspections are a challenge • Slower processors than a typical PC • Limited Imager Selection • No Integrated HMI • Limited on-board storage
- 18. 18ni.com HardwareArchitectures – LabVIEWRealTime Single Processor – Multi Imagers • Advantages: • Buy Processor / Software Licenses Once • Multi-image inspections processed together • Deterministic: No Naps • Super fast processors available • Broad Imager Selection • Integrated I/O with FPGA! • Disadvantages: • Limited onboard storage • No Integrated HMI*
- 19. 19ni.com HardwareArchitectures – LabVIEWRealTime Single Processor – Multi Imagers • Advantages: • Buy Processor / Software Licenses Once • Multi-image inspections processed together • Deterministic: No Naps • Super fast processors available • Broad Imager Selection • Integrated I/O with FPGA! • Integrated HMI with new NI Linux RT builds! • Disadvantages: • Limited onboard storage
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- 21. 21ni.com Case Study 1:Packaging Glue Inspection • Challenge: • Cartons vary in size • Frequent Job Changes • Inspect glue location relative to carton • 40 images per second
- 22. 22ni.com Case Study 1:Packaging Glue Inspection • Solution: • LabVIEW RT PC with FPGA card • 4 Basler Racer GigE line-scan cameras o 2 sets - side-by-side • Ultra-violet lighting and UV fluorescent glue • Custom one-button “Learn” algorithm
- 23. 23ni.com Case Study 1:Packaging Glue Inspection Algorithm: • Threshold image twice: o Carton Threshold – edges define coordinate system o Glue Threshold (brightest) • Inspect glue inside Regions of Interest (ROIs) • Configure “Ignore” regions
- 24. 24ni.com Packaging Glue Inspection– Passing Images
- 25. 25ni.com Packaging Glue Inspection– Failing Images
- 26. 26ni.com Case Study 2:Packaging Braille Inspection • Challenge: • EU requires Braille on all OTC drug packaging • Operators don’t read Braille • Confirm all Braille Dots • Super fast: 50 cartons per second • Handle carton rotation
- 27. 27ni.com Case Study 2:Packaging Braille Inspection Solution: • LabVIEW RT PC and CameraLink card • Basler Sprint Line-Scan Camera o Line-scan – allows varying lengths of cartons o Uniformity of light along entire length of carton • Automatic “Learn” and Translate Text
- 28. 28ni.com Case Study 2:Packaging Braille Inspection Algorithm: • Normalize for uneven lighting across width of image • Angled lighting, dimples comprise a dark & light spot • Locate dots with dual-threshold particle-find • Pattern Alignment with “Principal Axis*” *See Wikipedia for more information on Principle Axis
- 29. 29ni.com Case Study 2:Packaging Braille Inspection
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- 31. 31ni.com FPGA triggering • UseNI’s Vision-RIO • Pre-programmed • Common triggering and result buffering functions • Learn more: http://www.ni.com/white-paper/14599/en/ • Build your own with LabVIEW FPGA Module. • Manage Encoders, Part Sensors, Lighting and Camera I/O– all with microsecond precision • Custom Synchronization and Buffering • As easy as programming in LabVIEW
- 32. 32ni.com FPGAtriggering Example 1:Partial Closing Frame • In a line-scan inspection, close and re-open the frame to begin a new image without missing a line. • For webs that are continuous, but registered. Next Product Start Next Frame Time (200 us/division) Encoder A Encoder B Product Sensor Line trigger Frame Trigger
- 33. 33ni.com FPGA triggering Example2: Result Tracking • Monitor Encoders/Triggers • Buffer and Output Pass/Fail results
- 34. 34ni.com FPGA triggering Example2: Result Tracking
- 35. ni.com Parallelism inMachineVision Doing these thingsat the same time: • Trigger Cameras • Acquire Images • Process Images • Handle Results • Display Images
- 36. 36ni.com Why Use Parallelism? Benefits: 1.Its faster! Costs: 1. More Data Tracking 2. More complex programming 3. Handshaking between distributed processes 4. System-level debugging
- 37. 37ni.com Parallelism – Example Independentplatforms help: FPGA / LabVIEW RT LabVIEW RT still does multiple things at once
- 38. 38ni.com Parallelism – Strategies 1.Pass ID info with each image – use to sync results • IMAQdx Buffer # • Camera Timestamp • Encoder Position 2. Push the result position down the line – use queues
- 39. 39ni.com Parallelism – Strategies Useexplicit processor selection for processing Leave a processor available for image acquisition.
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- 41. 41ni.com RapidAlgorithms are key tohigh-speed processing Use Fast Algorithms Instead: • Particle find and analysis • Coordinate-based rotations and translations • (“Just Math”) • Subimage analysis Avoid Slow Algorithms: • Pixel Rotations • Morphology (erode, dilate) • Edge find • Object Find • Pattern Match When possible – do less.
- 42. 42ni.com Speed Tip: Avoidfloating point math Math operations are generally fast (compared to most vision algorithms) To make math REALLY fast, be sure to perform only integer math.
- 43. 43ni.com Speed Tip: Avoidfloating point math Average Execution Time (doubles): 142.6ms
- 44. 44ni.com Speed Tip: Avoidfloating point math Average Execution Time (integers): 57.75ms 2.5x faster than doubles!
- 45. 45ni.com Speed Tip: ImageReferences • Creating and Disposing Takes Time! • Declare a set of temp static image references – use in sub-VIs as temporary images.
- 46. 46ni.com Speed Tip: ImageReferences Bad Example – IMAQ Count Objects 2.vi
- 47. 47ni.com Speed Tip: ImageReferences DMC’s Customized “Count Objects” VI:
- 48. 48ni.com Speed Tip: VIProfiler Use the profiler to find VI’s that use the most Total Time Total Time: 1060ms
- 49. 49ni.com Speed Tip: VIProfiler Eliminated repeated IMAQ Create/Destroys Total Time: 860ms About 20% faster
- 50. 50ni.com Speed Tip: Sub-ImageAnalysis Wherepossible: • Process a smaller Region of Interest • Perform full-image operations on a down-sampled image. IMAQ Extract 2.vi:
- 51. ni.com Custom Product Training Forhigh-speed vision
- 52. 52ni.com Product training Motivation: • 10or more product changes per shift • Customers demand simplicity • Setup engineers are not provided This is the one thing we can take our time on – it’s only done once.
- 53. 53ni.com Product training –Braille Example Challenge: – Train and read with 1 button – Inspect Fast – 50 cartons/s EVERY DOT EVERY CARTON LEARY BRAILLE
- 54. 54ni.com Product training –Braille Example 2. Capture multiple setup images 3. Align each pattern based on Centroid and Principle Axis 4. Create Golden Template as average of data from multiple images 5. Re-inspect all template images with the new golden template 1. Capture longest possible image, determine carton size
- 55. 55ni.com Product training –Braille Example
- 56. 56ni.com Product training –Glue Example Challenge: – Train with 1 button • Define coordinate system • Define inspection regions – Inspect fast – 40 cartons/sec
- 57. 57ni.com Product training –Glue Example Training Algorithm 1. Acquire multiple images 2. Align carton coordinate systems 3. Compile glue positions into one master template 4. Add constant offsets to create inspection regions 5. Reinspect all individual images • Each inspection region is inspected as a subimage
- 58. 58ni.com Product training –Glue Example
- 59. 59ni.com Product training –Glue Example
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- 61. 61ni.com Conclusion • Machines aregoing faster • Zero defects are expected High-speed vision offers a competitive advantage as part of a high-tech product portfolio.
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