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Copyright © 2017 Tyco Innovation Garage 1
Divya Jain, Technical Director
May 2017
End to End Fire Detection Deep Neural
Network Platform
Copyright © 2017 Tyco Innovation Garage 2
• In-house Innovation Hub – Critical
Technologies
• Customer Centric – Internal and
External Partnerships
• Small Teams – Lean Startup Culture
• Efficient Development Cycles –
Iterative Output in Fast Sprints
• POCs, Demos and Validated
Learning
Tyco Innovation Garage
IOT
&
BlockChain
Deep
learning
&
Computer
Vision
3D
&
Frontier
Technologies
Big Data
&
Machine
Learning
Copyright © 2017 Tyco Innovation Garage 3
INTRODUCTION – Fire damage statistics
• 1,298,000 fires reported in US
• 494,000 were structure fires
• 2,860 civilian deaths
• 13,425 civilian injuries
• $9.8 billion in property damage
• One structure fire every 64 seconds
Source http://www.nfpa.org/research/reports-and-statistics/
Copyright © 2017 Tyco Innovation Garage 4
• Latency of smoke detectors is high for high-ceiling buildings
• Warehouses, auditoriums, storage structures, etc.
• Computer vision models are vulnerable
• Illumination, Background, Obstacles
• Video based solution for fire detection has several advantages
• Faster response time with cheaper cameras
• Safer, more secure and easier to use
• More contextual information to help fire fighters
• More effective than smoke detectors in certain scenarios
Motivation
Copyright © 2017 Tyco Innovation Garage 5
Overall Flow of Information
Deployment Development
Video,
Images
Detection,
Classification
Data In cloud
Model Building
Final ModelCompress Model
Online
Offline
Copyright © 2017 Tyco Innovation Garage 6
• Variations
• Horizontal Flips
• Rotational Flips
• Crop
• Blur
• Representation
• YUV vs. RGB
• Higher accuracy results with
YUV than RGB
• Chrominance channel of YUV
• Optical Flow
• Capture the temporal aspect
Data Augmentation
Copyright © 2017 Tyco Innovation Garage 7
• Hybrid model to exploit the temporal nature of burning fire together with
spatial characteristics
Choice of Network Topology
CNN CNN CNN
LSTM LSTM LSTM
. . . .
. .
.
x1 x2 x
N
p1 p2 p
N
. . . . p*
Temporal
component
i1 i2 iN
Copyright © 2017 Tyco Innovation Garage 8
• The topology for the spatial component is similar to AlexNet
• Learning strategy and base learning rate are important considerations
CNN Training: Learning Strategy
Class
Probabilities
loss
Iterations
Copyright © 2017 Tyco Innovation Garage 9
1. Drop-outs
• Adds more diversity to the net
• Ensemble modeling effect
2. Regularization (weight-decay)
• Force the weights to be more diffuse
3. Gradient estimation
• Batch size selection to prevent optimization
from getting stuck at local minima
• Adding controlled amount of noise to
gradients
CNN Training: Combating Overfitting
Copyright © 2017 Tyco Innovation Garage 10
Results from CNN+LSTM Detector
• For each frame joint CNN+LSTM model predicts higher detection probabilities
• Probability values more consistent across the frames compared to spatial model
Results from a 3 feet fire video of length = 10 frames (10 seconds with 1 frame / s sampling)
Copyright © 2017 Tyco Innovation Garage 11
Images of Fire which were hard to detect
Small Obstacle
Background Illumination
Copyright © 2017 Tyco Innovation Garage 12
Variable Fire Heights Variable Lux
(illumination)
Performance Quantification
• Probabilities depends on field
of detection
• Closer Camera
• Big Fires
• Detection probability decreases
with increase in lux
• Distance is always a prominent
factor in probabilities
irrespective of other factors
Copyright © 2017 Tyco Innovation Garage 13
Real –Time Video
Raspberry Pi 3 Model B with USB camera
Quad core 1.2 Ghz Cortex A53, top of BCM2708 VideoCore 4 300 Mhz GPU (Broadcom), Armv7l 32 bit
Copyright © 2017 Tyco Innovation Garage 14
• Deployment Platforms
• nVidia Jetson, Intel Movidius, Qualcomm Snapdragon, Mythic-ai
• Different Fire Scenarios
• Blue Fire, Only Smoke, Outside Fire
• Different Use cases
• Human Action Recognition, Intrusion Detection, Snow Detection
What’s Next
Copyright © 2017 Tyco Innovation Garage 15
• http://www.fike.com/products/signifire-video-flame-smoke-intrusion-detection-system/
• http://www.atlantis-press.com/php/paper-
details.php?from=author+index&id=25850411&querystr=authorstr%3DX
• http://excel.fit.vutbr.cz/submissions/2015/099/99.pdf
• http://www.inf.fu-berlin.de/inst/ag-ki/rojas_home/documents/Betreute_Arbeiten/Master-
Hohberg.pdf
• Study of fire video detection
• Study of Video Image Fire Detection Systems for Protection of Large Industrial Applications
and Atria
References
Copyright © 2017 Tyco Innovation Garage 16
Questions?
THANK YOU!

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"End to End Fire Detection Deep Neural Network Platform," a Presentation from Tyco Innovation Edit view

  • 1. Copyright © 2017 Tyco Innovation Garage 1 Divya Jain, Technical Director May 2017 End to End Fire Detection Deep Neural Network Platform
  • 2. Copyright © 2017 Tyco Innovation Garage 2 • In-house Innovation Hub – Critical Technologies • Customer Centric – Internal and External Partnerships • Small Teams – Lean Startup Culture • Efficient Development Cycles – Iterative Output in Fast Sprints • POCs, Demos and Validated Learning Tyco Innovation Garage IOT & BlockChain Deep learning & Computer Vision 3D & Frontier Technologies Big Data & Machine Learning
  • 3. Copyright © 2017 Tyco Innovation Garage 3 INTRODUCTION – Fire damage statistics • 1,298,000 fires reported in US • 494,000 were structure fires • 2,860 civilian deaths • 13,425 civilian injuries • $9.8 billion in property damage • One structure fire every 64 seconds Source http://www.nfpa.org/research/reports-and-statistics/
  • 4. Copyright © 2017 Tyco Innovation Garage 4 • Latency of smoke detectors is high for high-ceiling buildings • Warehouses, auditoriums, storage structures, etc. • Computer vision models are vulnerable • Illumination, Background, Obstacles • Video based solution for fire detection has several advantages • Faster response time with cheaper cameras • Safer, more secure and easier to use • More contextual information to help fire fighters • More effective than smoke detectors in certain scenarios Motivation
  • 5. Copyright © 2017 Tyco Innovation Garage 5 Overall Flow of Information Deployment Development Video, Images Detection, Classification Data In cloud Model Building Final ModelCompress Model Online Offline
  • 6. Copyright © 2017 Tyco Innovation Garage 6 • Variations • Horizontal Flips • Rotational Flips • Crop • Blur • Representation • YUV vs. RGB • Higher accuracy results with YUV than RGB • Chrominance channel of YUV • Optical Flow • Capture the temporal aspect Data Augmentation
  • 7. Copyright © 2017 Tyco Innovation Garage 7 • Hybrid model to exploit the temporal nature of burning fire together with spatial characteristics Choice of Network Topology CNN CNN CNN LSTM LSTM LSTM . . . . . . . x1 x2 x N p1 p2 p N . . . . p* Temporal component i1 i2 iN
  • 8. Copyright © 2017 Tyco Innovation Garage 8 • The topology for the spatial component is similar to AlexNet • Learning strategy and base learning rate are important considerations CNN Training: Learning Strategy Class Probabilities loss Iterations
  • 9. Copyright © 2017 Tyco Innovation Garage 9 1. Drop-outs • Adds more diversity to the net • Ensemble modeling effect 2. Regularization (weight-decay) • Force the weights to be more diffuse 3. Gradient estimation • Batch size selection to prevent optimization from getting stuck at local minima • Adding controlled amount of noise to gradients CNN Training: Combating Overfitting
  • 10. Copyright © 2017 Tyco Innovation Garage 10 Results from CNN+LSTM Detector • For each frame joint CNN+LSTM model predicts higher detection probabilities • Probability values more consistent across the frames compared to spatial model Results from a 3 feet fire video of length = 10 frames (10 seconds with 1 frame / s sampling)
  • 11. Copyright © 2017 Tyco Innovation Garage 11 Images of Fire which were hard to detect Small Obstacle Background Illumination
  • 12. Copyright © 2017 Tyco Innovation Garage 12 Variable Fire Heights Variable Lux (illumination) Performance Quantification • Probabilities depends on field of detection • Closer Camera • Big Fires • Detection probability decreases with increase in lux • Distance is always a prominent factor in probabilities irrespective of other factors
  • 13. Copyright © 2017 Tyco Innovation Garage 13 Real –Time Video Raspberry Pi 3 Model B with USB camera Quad core 1.2 Ghz Cortex A53, top of BCM2708 VideoCore 4 300 Mhz GPU (Broadcom), Armv7l 32 bit
  • 14. Copyright © 2017 Tyco Innovation Garage 14 • Deployment Platforms • nVidia Jetson, Intel Movidius, Qualcomm Snapdragon, Mythic-ai • Different Fire Scenarios • Blue Fire, Only Smoke, Outside Fire • Different Use cases • Human Action Recognition, Intrusion Detection, Snow Detection What’s Next
  • 15. Copyright © 2017 Tyco Innovation Garage 15 • http://www.fike.com/products/signifire-video-flame-smoke-intrusion-detection-system/ • http://www.atlantis-press.com/php/paper- details.php?from=author+index&id=25850411&querystr=authorstr%3DX • http://excel.fit.vutbr.cz/submissions/2015/099/99.pdf • http://www.inf.fu-berlin.de/inst/ag-ki/rojas_home/documents/Betreute_Arbeiten/Master- Hohberg.pdf • Study of fire video detection • Study of Video Image Fire Detection Systems for Protection of Large Industrial Applications and Atria References
  • 16. Copyright © 2017 Tyco Innovation Garage 16 Questions? THANK YOU!