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UAV Image RecognitionTechnology and Applications             The UT UAV Group        Cockrell School of Engineering       ...
What is a UAV?• The Department of Defense Dictionary  defines a UAV as:  A powered, aerial vehicle that does not carry a  ...
Driving Technology•   Powered heavier than air flight•   Radio control (R/C)•   Autopilots•   GPS•   Imagery systems•   Hi...
1898          1912          1933          1960                  1903          1918          1959HISTORICAL FIRSTS
1898: First demonstration         of radio‐control  Nikola Tesla’s “Teleautomaton,” a radio‐control boat                  ...
1912: First autopilot                                               Elmer and Lawrence Sperry Curtiss B‐2• A gyrostabilize...
1918: First radio‐controlled unmanned flight                                   • Forerunner of the                        ...
1959: First unmanned reconnaissance aircraft                  Northrop Radioplane SD‐1 Falconer/Observer
TYPES OF UAVS
Fixed‐WingNorthrop Grumman RQ‐4 Global Hawk
Rotorcraft              Helicopter: Northrop Grumman MQ‐8 Fire                           Scout Quadcopter            Tiltr...
PAYLOADS
Electro‐optic Payload Systems• Optical Cameras• Low‐light‐level   (LLL) Cameras• Thermal Imagers
Radar Imaging Payloads• Synthetic   Aperture   Radar(SAR)
Dispensable Payloads                       • Military – Missiles• Civil ‐ Pesticides
UT UAV
Our Team           • Undergraduate           • Interdisciplinary           • Student leadership
AUVSI Competition• Student UAS   Competition in   Maryland• Reconnaissance   mission• Fourth year of   participation• 1st ...
Phoenix IIAirframe                        Avionics              Imagery
UT UAV Overview• Our Implementation  – Target Detection  – Target Analysis  – Position Determination
Target Characteristics• Position (LLA)• Background Shape• Background Color• Alphanumeric                       4 to 8 feet...
Target Detection• Color‐based approach  – Outlier image• Exploit target attributes  – Size, aspect ratio• Implemented on D...
Background Image• Represent image  in 3‐D color space  –                , • Image contains background and   foreground
Foreground Image• Average RGB pixels in frame                           0.06                                              ...
Outlier Image• Potential targets highlighted in oultlier  image      Original Image           Outlier Image
Binary Image• Remove noise ‐ windowed median filter• Label objects ‐ connected component     Binary Image             Labe...
Target Analysis                                    Bounding          Segmentation   Skeleton                        Compar...
Target Detection Performance• Tested on scaled airfield and recorded   video  – Robust to trees, runways  – Poor at detect...
ResultsLegend:      Correct      Incorrect       Marginally Incorrect 
Target Position Determination• Convert image coordinates to absolute position• Position Accuracy   – Maximum allowable err...
Monte Carlo Error Analysis                    Error Analysis (500 feet altitude)              Standard Deviation (feet)   ...
System OverviewSony FCB EX‐980S                               Target Analysis                                 LabVIEW     ...
Plans for 2012• Communication  – Switch to Wifi (802.11N)• Digital camera (DSLR)• Weight reduction
Why UAVs?• UAVs are suited for doing the “dull, dirty   and dangerous” tasks of everyday life.
Applications of UAVs in Texas• Oil & gas• Wildfires• Ranching
Oil & GasUse to check pipelines for leaks                                     Use as a method to collect and transmit     ...
Wildfires                                    Bastrop County WildfireAid Firefighters with real time   information and fire...
RanchingSpraying crops with pesticide and fertilizer, monitoring crops, soil, moisture, and pest conditions, and insect sa...
Safety• Due to safety concerns there are strict   regulations regarding the use of UAV’s in   unrestricted airspace throug...
Air Systems Lab• All the work done in the Air systems lab   are undergraduate student projects, for   various competitions. 
Q&A
Uav image recognition technology and applications
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Uav image recognition technology and applications

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Tejas P. Kulkarni - Cockrell School of Engineering

Presented at the 2011 Texas GIS Forum

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Transcript of "Uav image recognition technology and applications"

  1. 1. UAV Image RecognitionTechnology and Applications The UT UAV Group Cockrell School of Engineering The University of Texas at Austin Harmony Mones‐Murphy Chockalingam Viswanathan Tejas Kulkarni
  2. 2. What is a UAV?• The Department of Defense Dictionary defines a UAV as: A powered, aerial vehicle that does not carry a  human operator, uses aerodynamic forces to  provide vehicle lift, can fly autonomously or be  piloted remotely, can be expendable or  recoverable, and can carry a lethal or nonlethal  payload. 
  3. 3. Driving Technology• Powered heavier than air flight• Radio control (R/C)• Autopilots• GPS• Imagery systems• High density power batteries• Long range and low‐power micro radio devices• Miniaturized parts• Wireless networks• Powerful micro‐processors
  4. 4. 1898 1912 1933 1960 1903 1918 1959HISTORICAL FIRSTS
  5. 5. 1898: First demonstration  of radio‐control  Nikola Tesla’s “Teleautomaton,” a radio‐control boat  Electrical Exposition at Madison  Square Garden
  6. 6. 1912: First autopilot Elmer and Lawrence Sperry Curtiss B‐2• A gyrostabilizer hydraulically  operated the elevators and rudder.• Allowed the aircraft to fly straight  and level without pilot input.
  7. 7. 1918: First radio‐controlled unmanned flight • Forerunner of the  modern cruise missile. Curtiss‐Sperry Aerial Torpedo
  8. 8. 1959: First unmanned reconnaissance aircraft Northrop Radioplane SD‐1 Falconer/Observer
  9. 9. TYPES OF UAVS
  10. 10. Fixed‐WingNorthrop Grumman RQ‐4 Global Hawk
  11. 11. Rotorcraft Helicopter: Northrop Grumman MQ‐8 Fire  Scout Quadcopter Tiltrotor: Bell Eagle Eye
  12. 12. PAYLOADS
  13. 13. Electro‐optic Payload Systems• Optical Cameras• Low‐light‐level  (LLL) Cameras• Thermal Imagers
  14. 14. Radar Imaging Payloads• Synthetic  Aperture  Radar(SAR)
  15. 15. Dispensable Payloads • Military – Missiles• Civil ‐ Pesticides
  16. 16. UT UAV
  17. 17. Our Team • Undergraduate • Interdisciplinary • Student leadership
  18. 18. AUVSI Competition• Student UAS  Competition in  Maryland• Reconnaissance  mission• Fourth year of  participation• 1st in Autonomous  Target Recognition in  2010 
  19. 19. Phoenix IIAirframe Avionics Imagery
  20. 20. UT UAV Overview• Our Implementation – Target Detection – Target Analysis – Position Determination
  21. 21. Target Characteristics• Position (LLA)• Background Shape• Background Color• Alphanumeric 4 to 8 feet Character• Alphanumeric Color• Orientation 4 to 8 feet
  22. 22. Target Detection• Color‐based approach – Outlier image• Exploit target attributes – Size, aspect ratio• Implemented on DSP – Texas Instruments  C6748
  23. 23. Background Image• Represent image  in 3‐D color space – , • Image contains background and  foreground
  24. 24. Foreground Image• Average RGB pixels in frame 0.06 Red Plane 0.04 P e rc en t 0.02 0 0 50 100 150 200 255 Green Plane 0.04 P erc e nt 0.02 0 0 50 100 150 200 255 Blue Plane 0.04 P erc e nt 0.02 0 0 50 100 150 200 255 Pixel Intensty• Compute distance from mean• Distance threshold determines potential  targets
  25. 25. Outlier Image• Potential targets highlighted in oultlier image Original Image Outlier Image
  26. 26. Binary Image• Remove noise ‐ windowed median filter• Label objects ‐ connected component Binary Image Label Image
  27. 27. Target Analysis Bounding Segmentation Skeleton Compare Rectangle RotateCropped Image
  28. 28. Target Detection Performance• Tested on scaled airfield and recorded  video – Robust to trees, runways – Poor at detecting some colors Specification Performance Speed 10 frames per second Detection Accuracy* 85% False Positive Rate 10% * Accuracy = ratio of targets detected to total number of targets
  29. 29. ResultsLegend:      Correct      Incorrect       Marginally Incorrect 
  30. 30. Target Position Determination• Convert image coordinates to absolute position• Position Accuracy – Maximum allowable error – 150 feet – Desired error – less than 50 feet• Monte Carlo Error Analysis – Sweep camera 60 degrees in all directions from the  vertical – Estimate standard deviation of error
  31. 31. Monte Carlo Error Analysis Error Analysis (500 feet altitude) Standard Deviation (feet) 1000 750 45 500 40 250 35feet 0 30 -250 25 -500 20 -750 -1000 15 -750 -500 -250 0 250 500 750 1000 feet
  32. 32. System OverviewSony FCB EX‐980S  Target Analysis LabVIEW Texas Instruments  C6748 Triangle J Purple Yellow NW Lat Lon
  33. 33. Plans for 2012• Communication – Switch to Wifi (802.11N)• Digital camera (DSLR)• Weight reduction
  34. 34. Why UAVs?• UAVs are suited for doing the “dull, dirty  and dangerous” tasks of everyday life.
  35. 35. Applications of UAVs in Texas• Oil & gas• Wildfires• Ranching
  36. 36. Oil & GasUse to check pipelines for leaks  Use as a method to collect and transmit  data between rigs
  37. 37. Wildfires Bastrop County WildfireAid Firefighters with real time  information and firefighting  capability. 
  38. 38. RanchingSpraying crops with pesticide and fertilizer, monitoring crops, soil, moisture, and pest conditions, and insect sampling Use to track cattle/deer Check fences for holes
  39. 39. Safety• Due to safety concerns there are strict  regulations regarding the use of UAV’s in  unrestricted airspace throughout the  world. 
  40. 40. Air Systems Lab• All the work done in the Air systems lab  are undergraduate student projects, for  various competitions. 
  41. 41. Q&A
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