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

Tejas P. Kulkarni - Cockrell School of Engineering

Presented at the 2011 Texas GIS Forum

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Uav image recognition technology and applications Uav image recognition technology and applications Presentation Transcript

  • 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
  • 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. 
  • 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
  • 1898 1912 1933 1960 1903 1918 1959HISTORICAL FIRSTS
  • 1898: First demonstration  of radio‐control  Nikola Tesla’s “Teleautomaton,” a radio‐control boat  Electrical Exposition at Madison  Square Garden
  • 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.
  • 1918: First radio‐controlled unmanned flight • Forerunner of the  modern cruise missile. Curtiss‐Sperry Aerial Torpedo
  • 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 Tiltrotor: Bell Eagle Eye
  • 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 in Autonomous  Target Recognition in  2010 
  • 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 Character• Alphanumeric Color• Orientation 4 to 8 feet
  • Target Detection• Color‐based approach – Outlier image• Exploit target attributes – Size, aspect ratio• Implemented on DSP – Texas Instruments  C6748
  • Background Image• Represent image  in 3‐D color space – , • Image contains background and  foreground
  • 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
  • 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 Label Image
  • Target Analysis Bounding Segmentation Skeleton Compare Rectangle RotateCropped Image
  • 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
  • ResultsLegend:      Correct      Incorrect       Marginally Incorrect 
  • 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
  • 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
  • System OverviewSony FCB EX‐980S  Target Analysis LabVIEW Texas Instruments  C6748 Triangle J Purple Yellow NW Lat Lon
  • 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  data between rigs
  • Wildfires Bastrop County WildfireAid Firefighters with real time  information and firefighting  capability. 
  • 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
  • Safety• Due to safety concerns there are strict  regulations regarding the use of UAV’s in  unrestricted airspace throughout the  world. 
  • Air Systems Lab• All the work done in the Air systems lab  are undergraduate student projects, for  various competitions. 
  • Q&A