Xplomo

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