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Standoff IED Detection Using UAVs
Week 1: Original Focus
Hardware - A small, portable drone:
● that uses different sensors...
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....
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.”
- ...
Focusing on Our Primary Beneficiary
PRIMARY:
DISMOUNTED
INFANTRY
Most vulnerable to
attack
Relies on visual cues
(potholes...
Week 3: Mission Model Canvas
Value Proposition
Replace infantrymens’
capabilities to detect IEDs
A drone system
System to ...
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 ...
Pivot to Sensor Improvements
PLOMO
Hypothesis:
Sensors added to
drones like Raven will
increase situational
awareness and ...
Pivot to Sensor Improvements
PLOMO
Hypothesis:
Sensors added to
drones like Raven will
increase situational
awareness and ...
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...
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
Dr...
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 success...
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
Poth...
Week 8: Mission Model Canvas
Key Activities
Implement image
processing methods:
● Anomaly detection -
CFAR
● Machine learn...
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.
I...
Moving Forward
Buried IEDs
VBIED
Week 1 Week 10
H4D
Military
GIS Output
Dual
use
Agriculture
Forestry
Road maintenance
Bui...
Thank You
JIDO
Wayne A. Stanbery
W. Richards Thissell
James McGuyer
Industry Mentor
Kevin Ray
Robert Medve
Special Contrib...
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Xplomo Hacking for Defense 2017

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

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
  2. 103 Interviews 19 Government Affiliated 12 Image Processing Experts 54 Armed Forces Personnel18 Industry Affiliated PLOMO
  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
  4. The Journey PLOMO Hardware Software
  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
  7. Rock Bottom 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
  • tduenas

    Sep. 14, 2017

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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