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GaugeCam: An Image Based System to Measure Water Levels in Streams - PowerPoint PPT Presentation

Oct 28, 2023 •291 likes •380 views

Can we detect water level? Department of Biological and Agricultural Engineering GaugeCam: An Image Based System to Measure Water Levels in Streams Troy Gilmore, Franois Birgand, Kenneth Chapman, Andrew Brown Can we measure water level? The

  1. Can we detect water level? Department of Biological and Agricultural Engineering GaugeCam: An Image ‐ Based System to Measure Water Levels in Streams Troy Gilmore, François Birgand, Kenneth Chapman, Andrew Brown Can we measure water level? The System: Edge Detection and linkage to real world Detection measurement

  2. The System: Edge Detection The System: Edge Detection The System: Calibration The System: Edge Detection

  3. The System: Calibration Lab Research Objective: x = 0 cm, y = 75 cm Quantify source and magnitude of uncertainty when measuring water level with images Uncertainty: Sources Uncertainty: Three Experiments 1. Image Resolution Benchmark I 1. Image Resolution 2. Lighting effects 2. Lighting effects Benchmark II 3. Perspective 3. Perspective Water Level 4. Lens distortion 4. Lens distortion 5. Water meniscus 5. Water meniscus

  4. Uncertainty: Three Experiments Uncertainty: Benchmark I Benchmark I 1. Image Resolution 2. Lighting effects 3. Perspective v v v v 4. Lens distortion 5. Water meniscus Uncertainty: Benchmark I Uncertainty: Benchmark I v v v v 0.5 cm/pixel 0.25 cm/pixel

  5. Benchmark I: RESULTS Uncertainty Calculation • Many images per resolution Mean Bias, SD, RMSE (cm) • Error = measured – known value • Calculated distribution of errors for each resolution • Calculated bias, SD and RMSE of each distribution High res cm per pixel Lo res Uncertainty: Three Experiments Benchmark I: RESULTS ~ 5 m ~ 7 m Benchmark I 1. Image Resolution 16 mm lens 16 mm lens Mean Bias, SD, RMSE (cm) 2. Lighting effects Benchmark II 3. Perspective 4. Lens distortion 5. Water meniscus cm per pixel

  6. Uncertainty: Sources Benchmark II Perspective Lighting (Bloom) Lens Distortion Benchmark II: RESULTS Benchmark II: RESULTS Night Mean Bias, SD, RMSE (cm) Mean Bias, SD, RMSE (cm) 4 m RMSE Day 12 mm lens RMSE Std Dev Mean Bias 5 m 6 m 7 m cm per pixel cm per pixel

  7. Uncertainty: Camera effects Uncertainty: Three Experiments Benchmark I 1. Image Resolution Lighting* 2. Lighting effects* Benchmark II 3. Perspective Water Level 4. Lens distortion Meniscus Effects 5. Water meniscus Water Level: RESULTS Water Level: Posture Angle Mean Bias, SD, RMSE (cm) 10 deg 19 deg 5 m 4m, 12 mm 6 m 7 m For camera at 6 meters cm per pixel

  8. Water Level: 6m, 16mm lens Conclusions Bias (cm) 1. Lens distortion must be minimized 2. Posture angle may interact with meniscus 3. With reasonable precautions, accuracy of +/ ‐ 3 mm (0.01 ft) is achievable in the lab 19 10 posture angle from horizontal (degrees) Acknowledgements Salt Marsh Images: Randall Etheridge, Brad Smith Lab Analysis Assistance: Kelly Chapman Camera Equipment: www.Microseven.com www.Colorado ‐ Video.com Software: www.GaugeCam.com Check out our ASABE 2011 booth! Louisville Belle waterline

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