a deep neural network for counting vessels in sonar
play

A Deep Neural Network for Counting Vessels in Sonar Signals H. - PowerPoint PPT Presentation

Nov 16, 2022 •657 likes •854 views

A Deep Neural Network for Counting Vessels in Sonar Signals H. Aghdam, M. Bouchard, R. Laganiere, E. M. Petriu, P. Wort VIVA Lab, University of Ottawa The work was funded by General Dynamics Mission SystemsCanada The 33rd Canadian Conference

  1. A Deep Neural Network for Counting Vessels in Sonar Signals H. Aghdam, M. Bouchard, R. Laganiere, E. M. Petriu, P. Wort VIVA Lab, University of Ottawa The work was funded by General Dynamics Mission Systems–Canada The 33rd Canadian Conference on Artificial Intelligence May 2020

  2. Motivation Monitor sea borders and restricted areas ● Smuggles ○ Unauthorized access ○ Human operator ● Needs several operators to monitors a specific 24/7 ○ Training human operators can be costly and time consuming ○ Unusual activities are rare ○ We aim to automate this process using artificial intelligence 2

  3. Sonar Signals Sonar is used in underwater applications ● Active sonar ● Emits a signal and listens to the signal that is reflected after hitting an object or the ocean floor ○ Passive Sonar ● Listens to ambient noise and the signal generated by marine objects ○ Active sonar is not suitable detecting the presence of vessels or counting them in restricted areas since vessels can acquire the signal that is sent by the active sonar. 3

  4. How to Automate? Raw Sonar Spectrogram 1 Deep Neural Network 2 3 Absence of vessels (ie zero vessels) can be detected by computing second order statistics of the spectrograms 4

  5. Idea 1st patch Extract Features Time Frequency 5

  6. Idea (cont.) Extract Features 10 th patch Extract Features Time Frequency 6

  7. Idea (cont.) Extract Features Extract Features Time …….. k th patch Extract Features Frequency 7

  8. Idea (cont.) Extract Features Extract Features Time Integrate …….. k th patch Extract Features Frequency 8

  9. Idea (cont.) Extract Features Extract Features Time Integrate Predict …….. k th patch Extract Features Frequency 9

  10. Idea (cont.) Extract Features Extract Features Time Integrate Predict …….. k th patch Extract Features Frequency The goal is to perform this process end-to-end 10

  11. Idea (cont.) Extract Features Extract Features Time Integrate Predict …….. k th patch Extract Features Frequency Given an input of TxF, we must design a network whose receptive field in the last feature extraction layer is 𝛽 TxF where 𝛽 < 1 11

  12. Proposed Neural Networks 12

  13. Proposed Neural Networks 𝛽 TxF receptive field Frequency Time 13

  14. Experiments Synthesized a dataset ● 84K training samples ○ 6K validation samples ○ 27K test samples ○ Each sonar sample is synthesized using the following parameters: ● Heading ○ Bearing ○ Speed ○ Noise strength ○ Number of vessels ○ Range ○ Frequencies ○ Power ○ 14

  15. Results 15

  16. Results 2 targets 3 targets 1 target 16

  17. Comparison 17

  18. Conclusion Proposed a network to count the number of vessels in sonar signals ● Synthesizes a large dataset of passive sonar signals ● The results show a superior performance compared to traditional approaches ● Aim to use many-to-many predictions instead of many-to-one approach in this ● paper Improve our simulators and generate more challenging signals ● 18

  19. Thank You https://gitlab.com/haghdam/deep_vessel_counting 19

Recommend

Deep Learning Primer Nishith Khandwala Neural Networks Overview Neural Network Basics

Deep Learning Primer Nishith Khandwala Neural Networks Overview Neural Network Basics

Deep Learning Primer Nishith Khandwala Neural Networks Overview Neural Network Basics Activation Functions Stochastic Gradient Descent (SGD) Regularization (Dropout) Training Tips and Tricks Neural Network (NN)

828 views • 45 slides
Deep Compression and EIE: Deep Neural Network Model Compression and Efficient Inference

Deep Compression and EIE: Deep Neural Network Model Compression and Efficient Inference

Deep Compression and EIE: Deep Neural Network Model Compression and Efficient Inference Engine Song Han CVA group, Stanford University Apr 7, 2016 1 Intro about me and my advisor Fourth year PhD with Prof. Bill Dally at Stanford.

2.06k views • 70 slides
Deep Learning (jkim@bi.snu.ac.kr) 2015/05/7

Deep Learning (jkim@bi.snu.ac.kr) 2015/05/7

Deep Learning (jkim@bi.snu.ac.kr) 2015/05/7 1 History of Neural Network Research Neural network Deep belief net Back propagation Science

1.27k views • 108 slides
Deep Compression and EIE: Deep Neural Network Model Compression and Efficient Inference

Deep Compression and EIE: Deep Neural Network Model Compression and Efficient Inference

Deep Compression and EIE: Deep Neural Network Model Compression and Efficient Inference Engine Song Han CVA group, Stanford University Jan 6, 2015 A few words about us Fourth year PhD with Prof. Bill Dally at Stanford.

966 views • 54 slides
Concise Introduction to Deep Neural Networks Outline: Classification problems Motivating

Concise Introduction to Deep Neural Networks Outline: Classification problems Motivating

Concise Introduction to Deep Neural Networks Outline: Classification problems Motivating Deep (large) Neural Network (DNN) classifiers Neurons and DNN architectures Numerical training of DNNs (supervised deep learning) Spiking

630 views • 33 slides
Neural Networks: What can a network represent Deep Learning, Fall 2020 1 Recap : Neural

Neural Networks: What can a network represent Deep Learning, Fall 2020 1 Recap : Neural

Neural Networks: What can a network represent Deep Learning, Fall 2020 1 Recap : Neural networks have taken over AI Tasks that are made possible by NNs, aka deep learning Tasks that were once assumed to be purely in the human domain

1.92k views • 150 slides
Propagating Error Backward Hyperparameters for Neural Networks } Multi-layer (deep) neural

Propagating Error Backward Hyperparameters for Neural Networks } Multi-layer (deep) neural

Learning in Neural Networks w 1,3 w 3,5 1 3 5 w w 1,4 3,6 w w 2,3 4,5 2 4 6 w w 2,4 4,6 Class #18: Back-Propagation; } A neural network can learn a classification function by Tuning Hyper-Parameters adjusting its weights to

259 views • 4 slides
Neural Networks: What can a network represent Deep Learning, Spring 2018 1 Recap : Neural

Neural Networks: What can a network represent Deep Learning, Spring 2018 1 Recap : Neural

Neural Networks: What can a network represent Deep Learning, Spring 2018 1 Recap : Neural networks have taken over AI Tasks that are made possible by NNs, aka deep learning 2 Recap : NNets and the brain In their basic form, NNets

2.14k views • 145 slides
IMPLEMENTATION OF A PARALLEL BATCH TRAINING ALGORITHM FOR DEEP NEURAL NETWORK YUPING LIN IFLYTEK

IMPLEMENTATION OF A PARALLEL BATCH TRAINING ALGORITHM FOR DEEP NEURAL NETWORK YUPING LIN IFLYTEK

IMPLEMENTATION OF A PARALLEL BATCH TRAINING ALGORITHM FOR DEEP NEURAL NETWORK YUPING LIN IFLYTEK LABORATORY FOR NEURAL COMPUTING FOR MACHINE LEARNING DEPARTMENT OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCE YORK UNIVERSITY, TORONTO NOVEMBER

211 views • 20 slides
EN. 601.467/667 Introduc3on to Human Language Technology Deep Learning I Shinji Watanabe 1

EN. 601.467/667 Introduc3on to Human Language Technology Deep Learning I Shinji Watanabe 1

EN. 601.467/667 Introduc3on to Human Language Technology Deep Learning I Shinji Watanabe 1 Todays agenda Introduction of deep neural network Basics of neural network 2 Short bio Research interests Automatic speech

1.71k views • 62 slides
Neural Network Approach for Photon-counting Detection The First Step: PPE Correction Ruibin

Neural Network Approach for Photon-counting Detection The First Step: PPE Correction Ruibin

Neural Network Approach for Photon-counting Detection The First Step: PPE Correction Ruibin Feng, Ph.D. Ge Wang, Ph.D. David Rundle Biomedical Imaging Center, Biomedical Imaging Center, JairiNovus Technologies Ltd. CBIS/BME, RPI

552 views • 26 slides
How to Evaluate Efficient Deep Neural Network Approaches Vivienne Sze ( @eems_mit)

How to Evaluate Efficient Deep Neural Network Approaches Vivienne Sze ( @eems_mit)

How to Evaluate Efficient Deep Neural Network Approaches Vivienne Sze ( @eems_mit) Massachusetts Institute of Technology In collaboration with Yu-Hsin Chen, Joel Emer, Yannan Wu, Tien-Ju Yang, Google Mobile Vision Team Slides available at

692 views • 48 slides
Deep Learning with Neural Networks The Structure and Optimization of Deep Neural Networks Allan

Deep Learning with Neural Networks The Structure and Optimization of Deep Neural Networks Allan

Deep Learning with Neural Networks The Structure and Optimization of Deep Neural Networks Allan Zelener Machine Learning Reading Group January 7 th 2016 The Graduate Center, CUNY Objectives Explain some of the trends of deep learning and

680 views • 44 slides
FLEET: Flexible Efficient Ensemble Training for Heterogenous Deep Neural Networks Hui Guan,

FLEET: Flexible Efficient Ensemble Training for Heterogenous Deep Neural Networks Hui Guan,

FLEET: Flexible Efficient Ensemble Training for Heterogenous Deep Neural Networks Hui Guan, Laxmikant Kishor Mokadam, Xipeng Shen, Seung-Hwan Lim, Robert Patton 1 Build an image classifier? Deep Neural Network (DNN) CPU GPU Training

957 views • 41 slides
-- Deep Neural Network or Gaussian Mixture Model? Dong Yu Microsoft Research Thanks to my

-- Deep Neural Network or Gaussian Mixture Model? Dong Yu Microsoft Research Thanks to my

Who Can Understand Your Speech Better -- Deep Neural Network or Gaussian Mixture Model? Dong Yu Microsoft Research Thanks to my collaborators: Li Deng, Frank Seide, Gang Li, Mike Seltzer, Jinyu Li, Jui-Ting Huang, Kaisheng Yao, Adam

967 views • 53 slides
Deep Convolutional Neural Network for Computer Vision Products LI XU, R&amp;D Director

Deep Convolutional Neural Network for Computer Vision Products LI XU, R&amp;D Director

Deep Convolutional Neural Network for Computer Vision Products LI XU, R&amp;D Director SenseTime Group Limited SenseTime Introduction SenseTime focuses on invention and development of computer vision and deep learning technologies. Our

1.03k views • 39 slides
Deep Learning Jiseob Kim (jkim@bi.snu.ac.kr) Artificial Intelligence Class of 2016 Spring Dept.

Deep Learning Jiseob Kim (jkim@bi.snu.ac.kr) Artificial Intelligence Class of 2016 Spring Dept.

Deep Learning Jiseob Kim (jkim@bi.snu.ac.kr) Artificial Intelligence Class of 2016 Spring Dept. of Computer Science and Engineering Seoul National University 1 History of Neural Network Research Neural network Deep belief net Back

1.3k views • 108 slides
Efficient Neural Network Compression Namhoon Lee University of Oxford 3 May 2019 A Challenge in

Efficient Neural Network Compression Namhoon Lee University of Oxford 3 May 2019 A Challenge in

Efficient Neural Network Compression Namhoon Lee University of Oxford 3 May 2019 A Challenge in Deep Learning: Overparameterization Large neural networks require: memory &amp; computations power consumption A Challenge in Deep Learning:

722 views • 49 slides
6. Convolutional Neural Networks CS 535 Deep Learning, Winter 2018 Fuxin Li With materials from

6. Convolutional Neural Networks CS 535 Deep Learning, Winter 2018 Fuxin Li With materials from

6. Convolutional Neural Networks CS 535 Deep Learning, Winter 2018 Fuxin Li With materials from Zsolt Kira Quiz coming up Next Monday (2/5) 30 minutes Topics: Optimization Basic neural networks Neural Network

619 views • 41 slides
Sony's deep learning software &quot;Neural Network Libraries/Console and its use cases in Sony

Sony's deep learning software &quot;Neural Network Libraries/Console and its use cases in Sony

Sony's deep learning software &quot;Neural Network Libraries/Console and its use cases in Sony Yoshiyuki Kobayashi Senior Machine Learning Researcher Sony Network Communications Inc. / Sony Corporation GTC 2018 Sony's deep learning

401 views • 21 slides
for Speech Synthesis and Sensor Data Augmentation Deep Generative Neural Network Speech Text

for Speech Synthesis and Sensor Data Augmentation Deep Generative Neural Network Speech Text

Deep Generative Modeling for Speech Synthesis and Sensor Data Augmentation Deep Generative Neural Network Speech Text Praveen Narayanan Ford Motor Company PROJECT DESCRIPTION Use of DNNs increasingly prevalent as a solution for many data

893 views • 53 slides
EN. 601.467/667 Introduction to Human Language Technology Deep Learning II Shinji Watanabe 1

EN. 601.467/667 Introduction to Human Language Technology Deep Learning II Shinji Watanabe 1

EN. 601.467/667 Introduction to Human Language Technology Deep Learning II Shinji Watanabe 1 Todays agenda Basics of (deep) neural network How to integrate DNN with HMM Recurrent neural network Language modeling

1.33k views • 60 slides
Neural Network Optimization 1 CS 519: Deep Learning, Winter 2018 Fuxin Li With materials from

Neural Network Optimization 1 CS 519: Deep Learning, Winter 2018 Fuxin Li With materials from

Neural Network Optimization 1 CS 519: Deep Learning, Winter 2018 Fuxin Li With materials from Zsolt Kira Backpropagation learning of a network The algorithm 1. Compute a forward pass on the compute graph (DAG) from the input to all the

407 views • 16 slides
PERSISTENT AND UNFORGEABLE WATERMARKS FOR DEEP NEURAL NETWORKS Huiying Li, Emily Willson, Heather

PERSISTENT AND UNFORGEABLE WATERMARKS FOR DEEP NEURAL NETWORKS Huiying Li, Emily Willson, Heather

DNNS ARE INCREASINGLY POPULAR PERSISTENT AND UNFORGEABLE WATERMARKS FOR DEEP NEURAL NETWORKS Huiying Li, Emily Willson, Heather Zheng, Ben Y. Zhao Hey Siri Oct 30, 2019 1 2 DEEP NEURAL NETWORK (DNN) DNNS ARE HARD TO TRAIN A machine

115 views • 9 slides

More recommend