9/13/2015Memorial University of Newfoundland Faculty of Engineering & Applied Science Engineering...
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11/03/22 Memorial University of Newfoundland Memorial University of Newfoundland Faculty of Engineering & Applied Science Engineering 7854 Industrial Machine Vision INTRODUCTION TO MACHINE VISION II Prof. Nick Krouglicof
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Transcript of 9/13/2015Memorial University of Newfoundland Faculty of Engineering & Applied Science Engineering...
04/19/23Memorial University of Newfoundland
Memorial University of Newfoundland
Faculty of Engineering & Applied Science
Engineering 7854
Industrial Machine Vision
INTRODUCTION TO MACHINE VISION II
Prof. Nick Krouglicof
04/19/23Memorial University of Newfoundland
Presentation Outline
1. Machine vision systems for mechanical metrology
•Algorithms for camera calibration•Development of 3D vision systems for MI Flume Tank
2. Industrial Applications of Machine Vision
•High speed, line scan camera-based inspection system for the food processing industry•Vision based inspection of liquid crystal display (LCD) modules
04/19/23Memorial University of Newfoundland
A systematic approach to the calibration ofmachine vision systems for industrial metrology
• In the field of machine vision, camera calibration refers to the experimental determination of a set of parameters which describe the image formation process for a given analytical model of the machine vision system.
• Ideally, camera calibration is performed without specialized optical equipment, without modifications to the hardware, and without a priori knowledge of the vision system.
• Most calibration techniques are based on the observation of planar (2D) targets with a large number of control points.
04/19/23Memorial University of Newfoundland
A systematic approach to the calibration ofmachine vision systems for industrial metrology
The machine vision parameters which must be identified include :
a) The scale factor b) The frame buffer coordinates of the image center c) The effective focal length of the lens-camera assemblyd) The radial lens distortion coefficiente) The pose (position and orientation) of the camera
Parameters a) through d) are classified as intrinsic, e) as extrinsic.
04/19/23Memorial University of Newfoundland
A systematic approach to the calibration ofmachine vision systems for industrial metrology
04/19/23Memorial University of Newfoundland
A systematic approach to the calibration ofmachine vision systems for industrial metrology
04/19/23Memorial University of Newfoundland
An Efficient Camera Calibration Technique OfferingRobustness and Accuracy Over a Wide Range
of Lens Distortion
04/19/23Memorial University of Newfoundland
An Efficient Camera Calibration Technique OfferingRobustness and Accuracy Over a Wide Range
of Lens Distortion
CALIBRATION ACCURACY AND THE LENS DISTORTION MODEL
04/19/23Memorial University of Newfoundland
Underwater 3D Vision Systems for MI Flume Tank
04/19/23Memorial University of Newfoundland
Prototype Underwater Stereo Vision System
04/19/23Memorial University of Newfoundland
Calibration Target for Underwater Stereo Vision System
04/19/23Memorial University of Newfoundland
Development of an Intelligent 3D Vision System for Underwater Environment through Actively
Manipulated Laser Triangulation
04/19/23Memorial University of Newfoundland
Development of an Intelligent 3D Vision System for Underwater Environment through Actively
Manipulated Laser Triangulation
04/19/23Memorial University of Newfoundland
Industrial Machine Vision Applications
04/19/23Memorial University of Newfoundland
Image Analysis Tools for Automated Inspection in the Food Processing Industry: X-Ray Enhancement
04/19/23Memorial University of Newfoundland
Image Analysis Tools for Automated Inspection in the Food Processing Industry: Multispectral Imaging
04/19/23Memorial University of Newfoundland
Image Analysis Tools for Automated Inspection in the Food Processing Industry: Multispectral Imaging
04/19/23Memorial University of Newfoundland
High Speed, Line Scan Based Inspection System
04/19/23Memorial University of Newfoundland
•Objective: To remove defects (I.e. visible, dark particle larger than 0.007”) from apple sauce
•System must be able to handle 12 metric tons per 8 hour shift
•System must remove 95% of visible defects
Line Scan Based Inspection System: Specifications
04/19/23Memorial University of Newfoundland
•2 distinct challenges:
•Detection
•Removal
High Speed, Line Scan Based Inspection System
04/19/23Memorial University of Newfoundland
High Speed, Line Scan Based Inspection System
04/19/23Memorial University of Newfoundland
High Speed, Line Scan Based Inspection System
04/19/23Memorial University of Newfoundland
•Detection system based on a high performance line scan camera; 4096 pixels per line at 4800 lines per second.
•Image acquisition and processing functions implemented on a Complex Programmable Logic Device (CPLD) as opposed to a microprocessor or Digital Signal Processor (DSP).
•The objective is to implement image processing functions with dedicated logic gates (i.e. hardware) for real-time performance.
Line Scan Based Detection System
04/19/23Memorial University of Newfoundland
What are CPLDs?
•Complex Programmable Logic Devices (CPLDs) are a class of programmable logic device that are commonly used to implement complex digital designs on a single integrated circuit.
•Applications of CPLDs in the field of computer engineering include the implementation of bus controllers, address decoders, priority encoder and state machines
04/19/23Memorial University of Newfoundland
Line Scan Camera-Based Detection System
04/19/23Memorial University of Newfoundland
Line Scan Camera-Based Detection System
04/19/23Memorial University of Newfoundland
Line Scan Camera-Based Detection System
Typical Particle
Typical section of apple sauce recorded with an area scan camera
04/19/23Memorial University of Newfoundland
LINESCAN DATA
20
40
60
80
100
120
1 101 201 301 401 501 601 701
Pixel Number
Lig
ht
Lev
el
Line Scan Camera-Based Detection System
04/19/23Memorial University of Newfoundland
Line Scan Camera-Based Detection System
04/19/23Memorial University of Newfoundland
Removal System
04/19/23Memorial University of Newfoundland
Removal System
04/19/23Memorial University of Newfoundland
Removal System: Flow Characterization
•Rheological nomenclature and associated velocity profiles for steady flow through tubes with circular cross section.
04/19/23Memorial University of Newfoundland
Viscosity Measurement
04/19/23Memorial University of Newfoundland
Viscosity Measurement
Velocity profile can be characterized by power law!
04/19/23Memorial University of Newfoundland
Flow Profile of Apple Sauce
04/19/23Memorial University of Newfoundland
Particle Removal Window
04/19/23Memorial University of Newfoundland
System Timing Diagram
04/19/23Memorial University of Newfoundland
Aspiration Valve Characterization
04/19/23Memorial University of Newfoundland
Aspiration Valve Characterization
04/19/23Memorial University of Newfoundland
Particle Detection Rate Versus Flowrate
04/19/23Memorial University of Newfoundland
High Speed, Line Scan Based Inspection System:Current Status
•Industrial partner is currently developing the production version of the system.
•Packaging of the principle components (i.e., lenses, cameras, electronics, light sources) remains a major challenge given the environment.
•One possible solution is to integrate all the electronics in the camera enclosure.
•Partner is anxious to explore applications in the pulp and paper industry.
04/19/23Memorial University of Newfoundland
Vision Based Inspection of LiquidCrystal Display (LCD) modules
•Objective: to automate the inspection of LCD modules in order to improve quality control
•One step in the implementation of a Six-Sigma Program (“3.4 defects per million opportunities”)
•The inspection must be completed within 30 seconds for 10 predetermined LCD patterns
•System can “learn” new LCD modules without modifying software
04/19/23Memorial University of Newfoundland
Vision Based Inspection of LCD modules: System Components
•Pulnix camera with macro lens
•High frequency fluorescent light sources
•Coreco Bandit integrated image acquisition and VGA accelerator
•Software developed using with WiT graphical programming environment in combination with Microsoft VB
04/19/23Memorial University of Newfoundland
Vision Based Inspection of LiquidCrystal Display (LCD) modules
04/19/23Memorial University of Newfoundland
Original Image Showing Error in Alignment
Vision Based Inspection of LiquidCrystal Display (LCD) modules
04/19/23Memorial University of Newfoundland
Thresholding Operation – Image Subtraction with respect to an image with no segments
illuminated
Vision Based Inspection of LiquidCrystal Display (LCD) modules
04/19/23Memorial University of Newfoundland
Blob Analysis – Reference Points are Identified
Vision Based Inspection of LiquidCrystal Display (LCD) modules
04/19/23Memorial University of Newfoundland
Image Rotation and Translation
Vision Based Inspection of LiquidCrystal Display (LCD) modules
04/19/23Memorial University of Newfoundland
Pixel by Pixel Image Subtraction from Reference Image – Thinning Operator
Vision Based Inspection of LiquidCrystal Display (LCD) modules
04/19/23Memorial University of Newfoundland
Blob Analysis
Vision Based Inspection of LiquidCrystal Display (LCD) modules
04/19/23Memorial University of Newfoundland
Blob Analysis
Vision Based Inspection of LiquidCrystal Display (LCD) modules: Conclusions
•Inspection system was installed at BAE Systems Canada Ltd. where it was used to test between 200 and 600 LCD displays per day.
•Number of defective modules that passed inspection was basically reduced to zero.
•Occasional “false positives” proved to be technical problems with the devices that previously went unreported.
•Applications for this technology are numerous given the number of LCD displays produced annually.
04/19/23Memorial University of Newfoundland
QUESTIONS
?
