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Xplomo Hacking for Defense 2017

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Stanford University
Stanford University

mission model, mission model canvas, customer development, Hacking for Defense, lean startup, stanford, startup, steve blank, Pete Newell, Joe Felter, minimum viable product

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Standoff IED Detection Using UAVs Week 1: Original Focus Hardware - A small, portable drone: ● that uses different sensors to; ● replace human capabilities in detecting IEDs Week 10: Final Focus Software - A sense-making system: ● that uses image processing techniques to; ● augment human capabilities in detecting IEDs; and ● scales across platforms. 103 Interviews PLOMOJIDO Sponsor
103 Interviews 19 Government Affiliated 12 Image Processing Experts 54 Armed Forces Personnel18 Industry Affiliated PLOMO
Yicheng An Weihan Zhang Robert André Borochok Marko Jakovljevic M.S. Computer Vision & Machine Learning M.S. Business M.S. Management Science and Engineering Postdoc Imaging School of Medicine Software Engineering Law Enforcement / Strategy & Operations Industrial Engineering / Operations Radiology / Image Processing 3 PLOMO
The Journey PLOMO Hardware Software
Our Opening Hypotheses It’s all about the Hardware Multi-Functional Tool to Replace Human Detects All Types of IEDs PLOMO
Who’s our Beneficiary? … Everyone! PLOMO
Rock Bottom PLOMO
PLOMO Our problem scope is far too wide.
We Need to Truly Understand the Problem PLOMO
“Infantrymen are the most vulnerable to IED attacks...they are trained to detect IEDs, but rely mainly on visual cues.” - Operational Commander PLOMO
Focusing on Our Primary Beneficiary PRIMARY: DISMOUNTED INFANTRY Most vulnerable to attack Relies on visual cues (potholes, disturbed earth) Needs standoff detection capability Load is a major constraint Needs near real-time detection 11 PLOMO
Week 3: Mission Model Canvas Value Proposition Replace infantrymens’ capabilities to detect IEDs A drone system System to analyze drone feed and indicate risk areas to war fighter Beneficiaries Primary: Dismounted infantry patrolling known area 12 PLOMO
Let’s build a counter- IED drone for ground infantry... PLOMO
...Or not. “We already use surveillance drones…why would we need another one?” - Operational Commander PLOMO
Let’s Modify Existing Hardware! Build a Drone Add Sensors to Existing Drones PIVOT PLOMO
Pivot to Sensor Improvements PLOMO Hypothesis: Sensors added to drones like Raven will increase situational awareness and ease of IED detection. Reality: Sensors on their own do not give analytical insights
Pivot to Sensor Improvements PLOMO Hypothesis: Sensors added to drones like Raven will increase situational awareness and ease of IED detection. Reality: Sensors aren’t perfect “There is no vapor for chem sensors to sniff in an open environment.” - Explosive Signature Specialist
Pivot to Sensor Improvements PLOMO Hypothesis: Sensors added to drones like Raven will increase situational awareness and ease of IED detection. Reality: Adding sensors will require long deployment time “JIDO can’t … add a sensor to a program of record.” - JIDO Tech Chief
Hardware is Clearly not the Issue PLOMO
“My analysts spend hours staring at a screen to pick up anomalies. We still miss things from time to time.” - Intelligence analyst PLOMO
Getting out of the building PLOMO Camp Pendleton (San Diego)
Because of the near infinite number of ways an IED can be hidden, we limit our initial product to pothole detection. PLOMO
Week 5: Mission Model Canvas 23 Value Proposition Augment replace warfighters’ capabilities to detect IEDs via software Drone agnostic Pothole Detection PLOMO
Software is What’s Really Needed! Drone Hardware “Analyst in the hand” PIVOT PLOMO
We Have Found Xplomo’s Calling PLOMO
Our MVP and Beneficiary Needs Match. So Let’s Start Identifying Partners. PLOMO
So Who Do We Need to Work With? PLOMO
So Who Do We Need to Work With? PLOMO XPLOMO
Week 6: Mission Model Canvas Key Partners JIDO J8/J6 Image Processing Experts Mission Achievement - Algorithm that successfully detects potholes with a false alarm rate <5 per frame - At least 80% accuracy Buy-in/Support JIDO Troops in field Military Leadership Mission Achievement - Algorithm that successfully detects potholes with a false alarm rate <5 per frame - At least 80% accuracy
In the rush to develop a working product, you’ve got to Fail Fast, Move Quick PLOMO
Feature Detection Works…. ...Until It Doesn’t PLOMO ... Xplomo’s Experimental Results
PLOMO Academic Articles and 1 Paper Later...
Feature Detection Anomaly Detection Machine Learning PLOMO “Analyst in the hand” Successful Detection of Well-Defined Potholes Xplomo’s Experimental Results
Week 8: Mission Model Canvas Key Activities Implement image processing methods: ● Anomaly detection - CFAR ● Machine learning - YOLO Train the algorithms Package the software in JIDO compatible format PLOMO Deployment Algorithm refinement, Scaling, & Horizontal extension
Final Minimum Viable Product “Analyst-in-the-hand” Operator Any Aerial Platform PLOMO
Internal Readiness Level PLOMO Current product is an initial iteration of an algorithm that we will continue to improve. Internal Readiness Level
Moving Forward Buried IEDs VBIED Week 1 Week 10 H4D Military GIS Output Dual use Agriculture Forestry Road maintenance Building inspections PLOMO
Thank You JIDO Wayne A. Stanbery W. Richards Thissell James McGuyer Industry Mentor Kevin Ray Robert Medve Special Contributors Robert Best Caitlin Cima Todd Forsman Andrea Gilli George Hasseltine Rafi Holtzman Michael Leone David Zinn Special Thanks Camp Pendleton Commanders and Staff & to all our 103 Interviewees 38 Teaching Team Steve Blank Joseph Felter Peter Newell Steve Weinstein Teaching Assistants Darren Hau Isaac Matthews Melisa Tomak PLOMO

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Xplomo Hacking for Defense 2017

  • 1. Standoff IED Detection Using UAVs Week 1: Original Focus Hardware - A small, portable drone: ● that uses different sensors to; ● replace human capabilities in detecting IEDs Week 10: Final Focus Software - A sense-making system: ● that uses image processing techniques to; ● augment human capabilities in detecting IEDs; and ● scales across platforms. 103 Interviews PLOMOJIDO Sponsor
  • 3. Yicheng An Weihan Zhang Robert André Borochok Marko Jakovljevic M.S. Computer Vision & Machine Learning M.S. Business M.S. Management Science and Engineering Postdoc Imaging School of Medicine Software Engineering Law Enforcement / Strategy & Operations Industrial Engineering / Operations Radiology / Image Processing 3 PLOMO
  • 5. Our Opening Hypotheses It’s all about the Hardware Multi-Functional Tool to Replace Human Detects All Types of IEDs PLOMO
  • 6. Who’s our Beneficiary? … Everyone! PLOMO
  • 8. PLOMO Our problem scope is far too wide.
  • 9. We Need to Truly Understand the Problem PLOMO
  • 10. “Infantrymen are the most vulnerable to IED attacks...they are trained to detect IEDs, but rely mainly on visual cues.” - Operational Commander PLOMO
  • 11. Focusing on Our Primary Beneficiary PRIMARY: DISMOUNTED INFANTRY Most vulnerable to attack Relies on visual cues (potholes, disturbed earth) Needs standoff detection capability Load is a major constraint Needs near real-time detection 11 PLOMO
  • 12. Week 3: Mission Model Canvas Value Proposition Replace infantrymens’ capabilities to detect IEDs A drone system System to analyze drone feed and indicate risk areas to war fighter Beneficiaries Primary: Dismounted infantry patrolling known area 12 PLOMO
  • 13. Let’s build a counter- IED drone for ground infantry... PLOMO
  • 14. ...Or not. “We already use surveillance drones…why would we need another one?” - Operational Commander PLOMO
  • 15. Let’s Modify Existing Hardware! Build a Drone Add Sensors to Existing Drones PIVOT PLOMO
  • 16. Pivot to Sensor Improvements PLOMO Hypothesis: Sensors added to drones like Raven will increase situational awareness and ease of IED detection. Reality: Sensors on their own do not give analytical insights
  • 17. Pivot to Sensor Improvements PLOMO Hypothesis: Sensors added to drones like Raven will increase situational awareness and ease of IED detection. Reality: Sensors aren’t perfect “There is no vapor for chem sensors to sniff in an open environment.” - Explosive Signature Specialist
  • 18. Pivot to Sensor Improvements PLOMO Hypothesis: Sensors added to drones like Raven will increase situational awareness and ease of IED detection. Reality: Adding sensors will require long deployment time “JIDO can’t … add a sensor to a program of record.” - JIDO Tech Chief
  • 19. Hardware is Clearly not the Issue PLOMO
  • 20. “My analysts spend hours staring at a screen to pick up anomalies. We still miss things from time to time.” - Intelligence analyst PLOMO
  • 21. Getting out of the building PLOMO Camp Pendleton (San Diego)
  • 22. Because of the near infinite number of ways an IED can be hidden, we limit our initial product to pothole detection. PLOMO
  • 23. Week 5: Mission Model Canvas 23 Value Proposition Augment replace warfighters’ capabilities to detect IEDs via software Drone agnostic Pothole Detection PLOMO
  • 24. Software is What’s Really Needed! Drone Hardware “Analyst in the hand” PIVOT PLOMO
  • 25. We Have Found Xplomo’s Calling PLOMO
  • 26. Our MVP and Beneficiary Needs Match. So Let’s Start Identifying Partners. PLOMO
  • 27. So Who Do We Need to Work With? PLOMO
  • 28. So Who Do We Need to Work With? PLOMO XPLOMO
  • 29. Week 6: Mission Model Canvas Key Partners JIDO J8/J6 Image Processing Experts Mission Achievement - Algorithm that successfully detects potholes with a false alarm rate <5 per frame - At least 80% accuracy Buy-in/Support JIDO Troops in field Military Leadership Mission Achievement - Algorithm that successfully detects potholes with a false alarm rate <5 per frame - At least 80% accuracy
  • 30. In the rush to develop a working product, you’ve got to Fail Fast, Move Quick PLOMO
  • 31. Feature Detection Works…. ...Until It Doesn’t PLOMO ... Xplomo’s Experimental Results
  • 32. PLOMO Academic Articles and 1 Paper Later...
  • 33. Feature Detection Anomaly Detection Machine Learning PLOMO “Analyst in the hand” Successful Detection of Well-Defined Potholes Xplomo’s Experimental Results
  • 34. Week 8: Mission Model Canvas Key Activities Implement image processing methods: ● Anomaly detection - CFAR ● Machine learning - YOLO Train the algorithms Package the software in JIDO compatible format PLOMO Deployment Algorithm refinement, Scaling, & Horizontal extension
  • 35. Final Minimum Viable Product “Analyst-in-the-hand” Operator Any Aerial Platform PLOMO
  • 36. Internal Readiness Level PLOMO Current product is an initial iteration of an algorithm that we will continue to improve. Internal Readiness Level
  • 37. Moving Forward Buried IEDs VBIED Week 1 Week 10 H4D Military GIS Output Dual use Agriculture Forestry Road maintenance Building inspections PLOMO
  • 38. Thank You JIDO Wayne A. Stanbery W. Richards Thissell James McGuyer Industry Mentor Kevin Ray Robert Medve Special Contributors Robert Best Caitlin Cima Todd Forsman Andrea Gilli George Hasseltine Rafi Holtzman Michael Leone David Zinn Special Thanks Camp Pendleton Commanders and Staff & to all our 103 Interviewees 38 Teaching Team Steve Blank Joseph Felter Peter Newell Steve Weinstein Teaching Assistants Darren Hau Isaac Matthews Melisa Tomak PLOMO