The document discusses using drones and AI to count sheep by taking images of sheep with a drone, training a neural network model to identify sheep in images, and deploying the model on the drone to count sheep in real time and validate the counts. Key components include the DJI Mobile SDK, Google Tensorflow, collecting training data, training and evaluating models in Google Cloud, and deploying the model on the drone to test and validate sheep counts.

1. Counting Sheep with Drones and AI
Godfrey Nolan

3. 01.
Experience

4. Experience
• Agribotix
• Automated flight around field
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5. Experience
• Secure Drones
• No data leakage
• Tamper resistant
• Self destruct button
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6. Experience
• Warehouse
• AI Barcode reader
• Automated Inventory
• Publish to IoT topic
• Blockchain
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7. 02.
Counting Sheep??

8. Counting Sheep
• Requirements
• Semi-practical example
• Using existing experience
• Answer the age old question
‘Do Androids dream of
Electric Sheep’
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9. 03.
Plug and Play

10. Components
• DJI Mobile SDK
• Google Tensorflow
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11. Components
• DJI Mobile SDK
• Google Tensorflow
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12. Components
• DJI Mobile SDK
• Google Tensorflow
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13. 04.
Workflow

14. Workflow
• Get Data
• Train & Test
• Adjust
• Deploy
• Test & Validate
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15. Workflow
• Get Data
• Train & Test
• Adjust
• Deploy
• Test & Validate
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16. Workflow
• Get Data
• Train & Test
• Adjust
• Deploy
• Test & Validate
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17. Train and Test
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18. Workflow
• Get Data
• Train & Test
• Adjust
• Deploy
• Test & Validate
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19. Workflow
• Get Data
• Train & Test
• Adjust
• Deploy
• Test & Validate
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20. Workflow
• Get Data
• Train & Test
• Adjust
• Deploy
• Test & Validate
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21. Workflow
• Get Data
• Train & Test
• Adjust
• Deploy
• Test & Validate
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22. 05.
Implementation

23. Implementation
• Get Data
• Train & Test
• Adjust
• Deploy
• Test & Validate
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24. Implementation
• Get Data
• Train & Test
• Adjust
• Deploy
• Test & Validate
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25. Implementation
• Get Data
• Train & Test
• Adjust
• Deploy
• Test & Validate
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26. Implementation
• Get Data
• Train & Test
• Adjust
• Deploy
• Test & Validate
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27. Train and Test
• Prepare the Dataset
• Set Up Google Cloud Account
• Set Up Docker
• Configure Google Cloud
• Set Up Object Detection API
• Train Your Model
• Evaluate Your Model
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28. Train and Test

29. Implementation
• Get Data
• Train & Test
• Adjust
• Deploy
• Test & Validate
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30. Implementation
• Get Data
• Train & Test
• Adjust
• Deploy
• Test & Validate
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31. Implementation
• Get Data
• Train & Test
• Adjust
• Deploy
• Test & Validate
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32. 06.
Resources Q&A

33. Lessons Learned
• Crowdsource Earlier
• Choose More Practical Example
• Create Your Own Images
• Need Beta Testers
• Streamline Config. with Docker
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34. Resources
• http://developer.dji.com
• https://www.tensorflow.org/install/
• https://github.com/tzutalin/labelImg
• http://labelme.csail.mit.edu/Release3.0/
• https://github.com/tensorflow/models/tree/master/research/object_detection
• https://becominghuman.ai/
• https://docs.bazel.build
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35. Q&A
• godfrey@riis.com
• @godfreynolan
• riis.com/blog
• slides.com/godfreynolan
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