computer vision application in …sstdpa/ssiuisibc/isi ppt v2.pdfwhich makes profile inspection,...
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COMPUTER VISION APPLICATION IN
INDUSTRIES
Wg Cdr N. Radhakrishnan
TIFAC CORE IN
MACHINE VISION
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Topics for Discussion
About TIFAC-CORE in Machine Vision
Computer Vision
Fundamental Issues
Industry Applications – Automotive, Pharma, Electronic Industries
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About TIFAC CORE
DST Sponsored Programme
Objectives:
To set up State of the art laboratory in the area of MV and to undertake research activities.
To introduce the MV technology to the industries. Provide consultancy services, develop solutions, Train industry personnel.
To train the faculty members and students .
Director: Wg Cdr N. Radhakrishnan
Professor
Rajalakshmi Engineering College
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Machine Vision
Machine Vision: Making useful decisions about real physical objects and
scenes based on sensed images
Challenges: Understanding the Customers Requirement
Parameters to be inspected and its tolerance, Location of the System installation Light, heat, dust and other environemtnal condition Number of components to be inspected per sececond / minute or per
shift Automation requirements
Selection of Camera, Lens, Lighting System Find the field of view, estimate camera resolution requirement Select lens Select suitable light
Developing automation solution
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Cameras
Types of Camera
Area Scan vs Line Scan / Board Level Camera
CCD vs CMOS, Grey / Colour
Interface
Resolution
Global Shutter Vs Rolling Shutter
Sensor Format
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Camera Types
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Interfaces
Firewire : 400 Mbps
USB 2.0 / 3.0 : 480 Mbps / 5 Gbps
Gig E : 1 Gbps
Camera Link : 5.3Gbps
CoXpress : 6.2 Gbps
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Camera and Lens
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Fundamental Issues
Perspective:
Perspective errors
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Fundamental Issues (cont.)
Perspective and Distortion (Pin Cushion and barrel) Errors occur due to camera location problems.
Can be with spatial calibration techniques.
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Normal lens vs Telecentric Lens
Angle of view / Focal Length
Magnification / Depth of Field
F Stop
Lens Mount
LENS
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Lens Types
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Field of View, Focal Length and Standoff
Distance
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Lens and Aperture
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Lens Depth of Field
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Lens Selection
What to do if you need to change the image size?
To increase magnification (Smaller FOV)
Use a lens with longer focal length
Move the camera closer to the part
To decrease magnification (Larger FOV)
Use shorter focal length
Move the camera further from part
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LIGHTING
Bright Field Illumination
Dark Field Illumination
Back Light
Diffused Light
Oblique Lighting
Axial Lighting
Structured Lighting
Collimated Lighting
Doom Lighting
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Different types of Lighting
Description Images
LED Diffuse Line Light
660nm, Bright Field / Dark Field Ring Light
5.00 Inch, White, LED Darkfield Illuminator with
mounting bracket
660nm, LED Broad Area Linear Array
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Description Images
660nm, 100mm X 200mm, LED Backlight
660nm, 4" LED Apposelite with mounting bracket
50mm, White, Flat Dome Illuminator with power
supply
660nm, 2", LED Diffuse Axial Illuminator w/power
supply
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Description Images
670nm Micro-Focus Laser Line Diode, 10°,
13μm Line, Modulated with power supply
6" Hemilite Fluorescent Illuminator with power
supply
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Fundamental Issues
Lighting issues
Ambient Light, Sunlight Changes, Seasonal Changes should not compromise image analysis and processing.
Proper illumination leads to Faster processing time.
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Fundamental Issues (cont.)
Backlighting creates sharp contrasts which makes profile inspection, finding edges and measuring distances fast and easy.
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Normal Light vs Dome Light
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Axial Light vs Normal Light
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Dark Field Light vs Normal
Light
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Dark Field Light
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Application Software
Matlab
C ++
Open CV
Lab View
Halcon
Matrox Imaging Library
Mv Impact
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PROJECTS
AUTOMOTIVE INDUSTRIES
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Brake Piston Cylinder
Inspection Requirements:
Hole Burr
Hole Position
Hole Missing
Double Punch
Video
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Ok Burr
Algorithm: Canny edge detector Image processing steps 1. Colour to Gray level 2. Canny edge detector 3. Circle fit 4. Euclidean distance
Brake Piston Cylinder
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a. Captured Image
Brake Piston Cylinder
b. Output of edge detector c. Circle Fit d. Red pixel shows the detection of burr
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Brake Piston Cylinder
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Spline Gear
Inspection Requirements:
Double Broach Groove Miss Model Mix-up Chamfer Presence
Algorithm : Canny edge detector Image processing steps 1. Colour to Gray level 2. Canny edge detector 3. Euclidean distance
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Ok Double Broach
Spline Gear
•The number of red pixels give the width of tooth •For Ok sample the count will be more than double broached component
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Spline Gear
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Flywheel
Inspection Requirements:
Checking right direction of Ring before it is pressed on to Flywheel
Video
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With Chamfer Without Chamfer
Image processing steps 1. Colour to Gray level 2. Gray level to binary 3. Morphology operation (dilation) 4. Blob analysis
Flywheel
Algorithm 1. Morphology operation (dilation) 2. Blob analysis
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Flywheel
a. Output of Thresholding b. Closing c. Dilation d. Output of area filter
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Flywheel
a. Output of Thresholding b. Closing c. Dilation d. Output of area filter
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Flywheel
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Cage Sleeve
Inspection requirements:
Side View
Window Width
Stagger Height
Total Length
Pillar Width
Top View
Inner Diameter
Outer Diameter
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Side View
Top View
Cage Sleeve
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Forged Brake System
Component
Inspection requirements
Inward Bend
Outward Bend
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Forged Brake System
Component
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Oil Seal Inspection
Dimensional Accuracies of Inner / Outer Dameter
Presence / Absence of Spring
Surface Defects on all sides: Top, Bottom and on the periphery.
Tear Damage on the Outer Surface
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Two Wheeler Fuel Cock
Assembly
Presence of through holes
Presence of burr inside the holes.
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Two Wheeler Fuel Cock
Assembly
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Two Wheeler Fuel Cock
Assembly
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Metal Inserts Inspection
Missing chamfer, Height variation, Cut, Dent, Burr
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Automation Set Up
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Height Inspection
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Height Not OK
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Spring Quality Inspection
Loose Coil
Over Coil
Waviness
Damage
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Spring Quality Inspection
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Spring Quality Inspection
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Spring Quality Inspection
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Bar Code and Embossed OCR
Comparison
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Bar Code and Embossed OCR
Comparison
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Texture Analysis
a. Good textured surface b. Surface with crack
c. Surface with blow holes d. Surface with blemishes
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Image Processing steps •Preprocessing
It involved removal of dust particles based on surrounding pixels rather than using median filter.
•Texture analysis based on GLCM
a. Input image
b. Output of median filter c. Output of preprocessing based on the surrounding pixels
Texture Analysis
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Output of Texture Analysis
Texture Analysis
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Scratch detection
Inspection Requirements: To detect scratches on metal surface
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Scratch detection
Image processing steps •Detection of band of intensity levels in iteration. •Hausdorff dilation distance between the pixels between consecutive iterations.
Algorithm Hausdorff dilation distance.
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(a) Gray level image (b) Binary image at first
iteration
(c) Binary image at
second iteration
(d) Image obtained
after computing Hausdorff
distance between (b) and
(c)
(e) Binary image at
third iteration
(f) final image (Image obtained
after computing Hausdorff
distance between (d) and (e))
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Scratch detection
Input Image Triangle method (Histogram)
Global thresholding Based on hausdorff dilation distance
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Scratch detection
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Scratch detection
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Reed Valves
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Reed Valves
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Reed Valves
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Reed Valves
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Fruit Sorting Machine
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Fruit Sorting Machine
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Fruit Sorting Machine
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ELECTRONICS INDUSTRIES
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3G Equipment
Inspection requirements: Cap Presence Label Presence, Position, Orientation Fan Presence Video
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Mobile Panel
Inspection requirements:
Checking for Part Presence
Dimensional Verification
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Not OK
OK
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PHARMA INDUSTRIES
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Cataract Lens
Inspection Requirements: To measure the radius of curvature of fixation members Image Processing steps •Median filter •Gray level to binary •Dilation •Fill holes •Erosion •Image subtraction •Contour detection •Circle fit
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Cataract Lens
a. Median filter b. Thresholding
c. Dilation d. Fill holes
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e. Erosion f. Image subtraction
g. Erosion
Cataract Lens
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Cataract Lens
h. Final output
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Blister Pack
Inspection requirements: Missing Tablets
Broken Tablets
Color Sequence Verification
Label Reading
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Missing Tablet Inspection
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Color Sequence Identification
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Label Reading
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Needle
Inspection requirements: Tip Damage
Orientation
Glue Inspection
Video
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Ok Tip Damage
Needle
Image processing steps 1. Colour to Gray level 2. Canny edge detector 3. Euclidean distance 4. Morphology operations (Dilation and Hole Fill) for orientation
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Inspection Panel
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Barcode Reading
Reads the Barcode and print the label
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ANY QUESTIONS