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Why sewers? Why inspection?
What is a sewer lateral? Isn’t ‘lateral’ an adjective?
Why are laterals so important in sewer mains?
What are the problems?
How can computer vision be used to help solve them?
What topics in computer vision have been explored?
How did everything turn out? What should be changed?
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Sewers – A necessary utility
Some lines over a century old
Regular inspection and maintenance
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Pipe Inspection/Repair Vehicle  Inconvenient acronym
18”‐60” Variable Diameter Chassis
Electronics
Camera Mount
Tool Manipulator
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What’s all this?
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Area of stress concentration
Poor joining
Cracks and Breaks
Vegetation Infiltration 
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Laterals ‐ With whom does responsibility lie?
Cannot perform repairs past lateral joints
Line of Making‐Life‐Difficult
Sewer Main
Sewer Lateral
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Cannot just ignore laterals
 Repair work may clog them up
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Laterals need special attention
May need to be “plugged” for repair
 Prevent material ingress while repairs are occuring
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Point:
 We need to know where the laterals are, how big they are, their orientation and what condition they’re in 
Manual Inspection
 Costly
 Slow
 Human Error (fatigue, boredom, midlife crises…)
Scale of Problems
 Manageable in small doses…
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Colorado Springs Utility manages 160,000+ lateral joints
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Computer Vision
 Enables inspection of thousands of pipes with minimal operator interaction
 More predictable error
 Capable of extracting quantitative data from imagery (Pose, size, etc.)
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Structured Light
 Duran, Althoefer, Seneviratne – Pipe Inspection Using a Laser‐Based Transducer and Automated Analysis Techniques
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Stock Images – Most sewers inspected every 8 years
 Zhong, Schindler, Stamm – “Lateral Detection”
▪ Images taken parallel to pipe radius, rather than axis
▪ Preprocessing, Segmentation, Classification phases
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Purpose‐Built Optics
 Broadhurst, Pridmore, Taylor – “Sensing for Feature Identification in Sewers”
▪ Specialized lighting and camera orientation equipment
▪ Reflective Photometric Stereo
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Generalized Hough Transform
 Expresses a feature template as a table of gradient angles and distances from a centroid
Regular Hough Transform for left‐opening parabolas
 A(y0, x0, r)
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 ଴
 For ଴
ଶ
as the coordinates of the test pixel
 is heuristically determined based on detection range
 ଴ ଴ as coordinates of vertex
Can trim away uninteresting parts of edge images based on a priori knowledge
 In this case, it is known that the lateral will appear on the right‐hand‐side of the image
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&
=
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determined based on ~3‐ft detection range
Static value of From start of detection:
 20 hits, 3 misses, 10 false (1 feature)
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False Positives
 Detection Occurs when No Laterals are Present
 Figure out the GHT
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Implement Adaptive Parameters
 ‫ ݎ‬value changes automatically based on expected position of features in next frames
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Implement frame‐by‐frame voting
 Each frame votes for detected features
 Similar to Hough transform, but frames instead of edge elements
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Explore SIFT for frame‐to‐frame tracking
Implement Inspection Condition Validation
 Allow system to know when errors are likely due to poor lighting, etc
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Extract Lateral Pose
 Provide pose in a useful form for motion control
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Sewers
 They’re important and must be maintained
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Laterals
 Common location of problems
 Difficult to repair
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Computer Vision
 Capable of identifying laterals automatically
 Quantitative data
 Employed Hough transform to find parabolas corresponding to lateral joint edges
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Future Work
 Overall goal: Robustness