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Computer Vision with Deep Learning

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Capgemini
Capgemini

The document discusses computer vision with deep learning. It provides an overview of convolutional neural networks and their use in computer vision applications like image classification and object detection. Specifically, it discusses how CNNs use convolutional layers to learn visual features from images and provide examples of CNNs being used for pipeline defect classification and filler cap quality control.

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Computer Vision with Deep Learning OOP Munich, 07.02.2018, Andreas Eßbaumer
Computer Vision with Deep Learning | Andreas Eßbaumer ▪ History and Hype of Deep Learning and Convolution Neural Networks ▪ Visualization of Convolutions ▪ The three and a half types of Computer Vision applications ▪ How to implement Deep Learning for Computer Vision? ▪ Image Classification: Pipelines ▪ Object Detection: Filler caps © 2017 Capgemini. All rights reserved. 2 Overview
Computer Vision with Deep Learning | Andreas Eßbaumer 3© 2018 Capgemini. All rights reserved. A bit of history Source: Bennamoun, DeepLearn 2017
Computer Vision with Deep Learning | Andreas Eßbaumer 4© 2018 Capgemini. All rights reserved. A bit of history Source: Bennamoun, DeepLearn 2017
Computer Vision with Deep Learning | Andreas Eßbaumer Why the hype? – The ImageNet competition ImageNet: Over 10 Mio labeled images. Over 10.000 different labels. ImageNet Competition: Restrict the number of labels to 1000. Algorithms have to produce up to five labels per image.
Computer Vision with Deep Learning | Andreas Eßbaumer 6© 2017 Capgemini. All rights reserved. ILSVRC challenge
Computer Vision with Deep Learning | Andreas Eßbaumer © 2017 Capgemini. All rights reserved. 7 Using convolutions the network is able to learn valuable filters and extract the most important features ▪ Each convolutional layer generates many different representations of the image through filters ▪ Filters are in most cases of the sizes 3x3, 5x5 or 7x7 and are learned(!) ▪ Max-pooling areas reduces the size of the image and makes sure filters cover a larger area of the picture ▪ Generally the latter the layer the larger the area of an image which is covered by a filter and the higher the number of filters 10 Layer 1 + 2 3 4 5 6 7 8 9 13 14 15 16 1211 The neural net architecture of VGG 16 Image Source: https://blog.heuritech.com/2016/02/29/a-brief-report-of- the-heuritech-deep-learning-meetup-5/
Computer Vision with Deep Learning | Andreas Eßbaumer Visualization of convolution layers learned(!) by the network From millions of training images, the network learns that the most basic features to look for in an image are basic forms like edges, circles or smooth color transitions. Source: Visualizing and Understanding Convolutional Networks by Zeiler/Fergus arXiv:1311.2901
Computer Vision with Deep Learning | Andreas Eßbaumer © 2017 Capgemini. All rights reserved. 9 Visualization of convolution layers learned(!) by the network • Each convolutional layer consists of a high number of filters • Depending on the image some filters are more and differently applicable than others Source: https://github.com/yosinski/deep-visualization-toolbox https://www.youtube.com/watch?v=AgkfIQ4IGaM
Computer Vision with Deep Learning | Andreas Eßbaumer Visualization of convolution layers learned(!) by the network Working with the most basic features extracted by the first layers, the network is able to combine them in a larger area of the picture and find more complex structures like certain patterns and written text pieces. Source: Visualizing and Understanding Convolutional Networks by Zeiler/Fergus arXiv:1311.2901
Computer Vision with Deep Learning | Andreas Eßbaumer © 2017 Capgemini. All rights reserved. 11 Visualization of convolution layers learned(!) by the network the number of filters increases to hundreds of specialized filters Source: Visualizing and Understanding Convolutional Networks by Zeiler/Fergus arXiv:1311.2901
Computer Vision with Deep Learning | Andreas Eßbaumer © 2017 Capgemini. All rights reserved. 12 Visualization of convolution layers learned(!) by the network • The filters are purely learned via training data. • There is even a filter highly specialized on detection human faces. Source: https://github.com/yosinski/deep-visualization-toolbox https://www.youtube.com/watch?v=AgkfIQ4IGaM
Computer Vision with Deep Learning | Andreas Eßbaumer © 2017 Capgemini. All rights reserved. 13 There are three and a half types of computer vision applications Image Classification/Recognition Object Detection Image Transformation/FusionDetect similar images
Computer Vision with Deep Learning | Andreas Eßbaumer © 2017 Capgemini. All rights reserved. 14 The two major application types are image classification and object detection Image Classification/Recognition Object Detection • Evaluates the image as a whole • Can categorize different states or even abstract concepts • Looks for physical or at least visually existing objects • Returns multiple objects of various types and with their position
Computer Vision with Deep Learning | Andreas Eßbaumer < 15© 2017 Capgemini. All rights reserved.Source: fast.ai • state-of-the-art image classification model in 7 lines of code. • prerequisites are sufficient, representative, labelled training images and GPUs • high-level deep learning framework like Keras make CNNs easy to run • DL-Frameworks like run CNNs efficiently on GPUs. Implementing an Image Classification model is easy (at least easier than it seems)
Computer Vision with Deep Learning | Andreas Eßbaumer © 2017 Capgemini. All rights reserved. 16 To build an object detection model one can use the Tensorflow Object Detection API • No changes on the deep learning code is necessary • the training runs efficiently on GPUs because of Tensorflow • labelling by giving meta information about which types of objects can be found and where (in which box). • Labelling tools are available, e.g. labelImg • Leveraging pre-trained models Useful links: https://github.com/tensorflow/models/tree/master/research/object_detection https://github.com/tzutalin/labelImg
Computer Vision with Deep Learning | Andreas Eßbaumer 17© 2017 Capgemini. All rights reserved. • 3.5 million km of pipelines in the world (2014) • Many of these are more than 50 years old. • Pipelines operate under immense pressure: Even small cracks can have disastrous consequences An example: Defect classification for pipeline maintenance • Our client offers solutions for pipeline maintenance • According to regulation pipeline inspections need to be done every few years • pipeline inspection robots for non- destructive testing of pipeline integrity are used
Computer Vision with Deep Learning | Andreas Eßbaumer 18© 2017 Capgemini. All rights reserved. • Defect detection is very challenging • Firstly, the sensors produce huge amounts of data that need to be preprocessed and stored online, i.e. during the inspection run. • Secondly, analyzing the images requires special training and is very time intensive. Defect classification with ultrasound • Given an image of a region of interest, can you accurately predict the defect type? The use case A pipeline inspection robot in its rare clean state
Computer Vision with Deep Learning | Andreas Eßbaumer Deep Learning gave better results with little effort 0,00% 10,00% 20,00% 30,00% 40,00% 50,00% 60,00% 70,00% 80,00% 90,00% 100,00% CV InceptionV3 - 172+ InceptionV3 - 0+ Custom-V1 Defect classification results Recall Specificity • After the successful POC, industrialization is currently ongoing • Human experts have a strong assistance tool now • Lowering risks by double-checks (Human + AI)
Computer Vision with Deep Learning | Andreas Eßbaumer © 2017 Capgemini. All rights reserved. 20 We use object detection to identify different types of defects and their position on a filler cap • Using a deep learning pipeline of two models to reach full flexibility • The first object detection model runs continuously and looks for various objects e.g. filler caps. • a second object detection model looks for various defects on the surface of the filler cap Model 1 Model 2
Computer Vision with Deep Learning | Andreas Eßbaumer © 2017 Capgemini. All rights reserved. 21 Depending on the number and types of defects the quality check is passed or failed • invariant to different environmental lighting, backgrounds, angles, reflections etc. • Detects small defects like scratches, too much paint or punctual damages. • Even the positions of the defects are given by the model providing. ideal information for potential rework • Built in one day • Uses 100 labelled images per filler cap
Computer Vision with Deep Learning | Andreas Eßbaumer Andreas Eßbaumer Senior Data Scientist Capgemini Munich Olof-Palme-Straße 14 81829 Munich andreas.essbaumer@capgemini.com M: +49 151 4025 2219 Summary Convolutional Neural Networks (CNNs) • are the State-of-the-Art for Computer Vision • run via Open-Source-Frameworks • require GPUs and labelled Images • are (relatively) easy to run
A global leader in consulting, technology services and digital transformation, Capgemini is at the forefront of innovation to address the entire breadth of clients’ opportunities in the evolving world of cloud, digital and platforms. Building on its strong 50-year heritage and deep industry-specific expertise, Capgemini enables organizations to realize their business ambitions through an array of services from strategy to operations. Capgemini is driven by the conviction that the business value of technology comes from and through people. It is a multicultural company of 200,000 team members in over 40 countries. The Group reported 2016 global revenues of EUR 12.5 billion. About Capgemini Learn more about us at www.capgemini.com This message contains information that may be privileged or confidential and is the property of the Capgemini Group. Copyright © 2017 Capgemini. All rights reserved. People matter, results count.

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Computer Vision with Deep Learning

  • 1. Computer Vision with Deep Learning OOP Munich, 07.02.2018, Andreas Eßbaumer
  • 2. Computer Vision with Deep Learning | Andreas Eßbaumer ▪ History and Hype of Deep Learning and Convolution Neural Networks ▪ Visualization of Convolutions ▪ The three and a half types of Computer Vision applications ▪ How to implement Deep Learning for Computer Vision? ▪ Image Classification: Pipelines ▪ Object Detection: Filler caps © 2017 Capgemini. All rights reserved. 2 Overview
  • 3. Computer Vision with Deep Learning | Andreas Eßbaumer 3© 2018 Capgemini. All rights reserved. A bit of history Source: Bennamoun, DeepLearn 2017
  • 4. Computer Vision with Deep Learning | Andreas Eßbaumer 4© 2018 Capgemini. All rights reserved. A bit of history Source: Bennamoun, DeepLearn 2017
  • 5. Computer Vision with Deep Learning | Andreas Eßbaumer Why the hype? – The ImageNet competition ImageNet: Over 10 Mio labeled images. Over 10.000 different labels. ImageNet Competition: Restrict the number of labels to 1000. Algorithms have to produce up to five labels per image.
  • 6. Computer Vision with Deep Learning | Andreas Eßbaumer 6© 2017 Capgemini. All rights reserved. ILSVRC challenge
  • 7. Computer Vision with Deep Learning | Andreas Eßbaumer © 2017 Capgemini. All rights reserved. 7 Using convolutions the network is able to learn valuable filters and extract the most important features ▪ Each convolutional layer generates many different representations of the image through filters ▪ Filters are in most cases of the sizes 3x3, 5x5 or 7x7 and are learned(!) ▪ Max-pooling areas reduces the size of the image and makes sure filters cover a larger area of the picture ▪ Generally the latter the layer the larger the area of an image which is covered by a filter and the higher the number of filters 10 Layer 1 + 2 3 4 5 6 7 8 9 13 14 15 16 1211 The neural net architecture of VGG 16 Image Source: https://blog.heuritech.com/2016/02/29/a-brief-report-of- the-heuritech-deep-learning-meetup-5/
  • 8. Computer Vision with Deep Learning | Andreas Eßbaumer Visualization of convolution layers learned(!) by the network From millions of training images, the network learns that the most basic features to look for in an image are basic forms like edges, circles or smooth color transitions. Source: Visualizing and Understanding Convolutional Networks by Zeiler/Fergus arXiv:1311.2901
  • 9. Computer Vision with Deep Learning | Andreas Eßbaumer © 2017 Capgemini. All rights reserved. 9 Visualization of convolution layers learned(!) by the network • Each convolutional layer consists of a high number of filters • Depending on the image some filters are more and differently applicable than others Source: https://github.com/yosinski/deep-visualization-toolbox https://www.youtube.com/watch?v=AgkfIQ4IGaM
  • 10. Computer Vision with Deep Learning | Andreas Eßbaumer Visualization of convolution layers learned(!) by the network Working with the most basic features extracted by the first layers, the network is able to combine them in a larger area of the picture and find more complex structures like certain patterns and written text pieces. Source: Visualizing and Understanding Convolutional Networks by Zeiler/Fergus arXiv:1311.2901
  • 11. Computer Vision with Deep Learning | Andreas Eßbaumer © 2017 Capgemini. All rights reserved. 11 Visualization of convolution layers learned(!) by the network the number of filters increases to hundreds of specialized filters Source: Visualizing and Understanding Convolutional Networks by Zeiler/Fergus arXiv:1311.2901
  • 12. Computer Vision with Deep Learning | Andreas Eßbaumer © 2017 Capgemini. All rights reserved. 12 Visualization of convolution layers learned(!) by the network • The filters are purely learned via training data. • There is even a filter highly specialized on detection human faces. Source: https://github.com/yosinski/deep-visualization-toolbox https://www.youtube.com/watch?v=AgkfIQ4IGaM
  • 13. Computer Vision with Deep Learning | Andreas Eßbaumer © 2017 Capgemini. All rights reserved. 13 There are three and a half types of computer vision applications Image Classification/Recognition Object Detection Image Transformation/FusionDetect similar images
  • 14. Computer Vision with Deep Learning | Andreas Eßbaumer © 2017 Capgemini. All rights reserved. 14 The two major application types are image classification and object detection Image Classification/Recognition Object Detection • Evaluates the image as a whole • Can categorize different states or even abstract concepts • Looks for physical or at least visually existing objects • Returns multiple objects of various types and with their position
  • 15. Computer Vision with Deep Learning | Andreas Eßbaumer < 15© 2017 Capgemini. All rights reserved.Source: fast.ai • state-of-the-art image classification model in 7 lines of code. • prerequisites are sufficient, representative, labelled training images and GPUs • high-level deep learning framework like Keras make CNNs easy to run • DL-Frameworks like run CNNs efficiently on GPUs. Implementing an Image Classification model is easy (at least easier than it seems)
  • 16. Computer Vision with Deep Learning | Andreas Eßbaumer © 2017 Capgemini. All rights reserved. 16 To build an object detection model one can use the Tensorflow Object Detection API • No changes on the deep learning code is necessary • the training runs efficiently on GPUs because of Tensorflow • labelling by giving meta information about which types of objects can be found and where (in which box). • Labelling tools are available, e.g. labelImg • Leveraging pre-trained models Useful links: https://github.com/tensorflow/models/tree/master/research/object_detection https://github.com/tzutalin/labelImg
  • 17. Computer Vision with Deep Learning | Andreas Eßbaumer 17© 2017 Capgemini. All rights reserved. • 3.5 million km of pipelines in the world (2014) • Many of these are more than 50 years old. • Pipelines operate under immense pressure: Even small cracks can have disastrous consequences An example: Defect classification for pipeline maintenance • Our client offers solutions for pipeline maintenance • According to regulation pipeline inspections need to be done every few years • pipeline inspection robots for non- destructive testing of pipeline integrity are used
  • 18. Computer Vision with Deep Learning | Andreas Eßbaumer 18© 2017 Capgemini. All rights reserved. • Defect detection is very challenging • Firstly, the sensors produce huge amounts of data that need to be preprocessed and stored online, i.e. during the inspection run. • Secondly, analyzing the images requires special training and is very time intensive. Defect classification with ultrasound • Given an image of a region of interest, can you accurately predict the defect type? The use case A pipeline inspection robot in its rare clean state
  • 19. Computer Vision with Deep Learning | Andreas Eßbaumer Deep Learning gave better results with little effort 0,00% 10,00% 20,00% 30,00% 40,00% 50,00% 60,00% 70,00% 80,00% 90,00% 100,00% CV InceptionV3 - 172+ InceptionV3 - 0+ Custom-V1 Defect classification results Recall Specificity • After the successful POC, industrialization is currently ongoing • Human experts have a strong assistance tool now • Lowering risks by double-checks (Human + AI)
  • 20. Computer Vision with Deep Learning | Andreas Eßbaumer © 2017 Capgemini. All rights reserved. 20 We use object detection to identify different types of defects and their position on a filler cap • Using a deep learning pipeline of two models to reach full flexibility • The first object detection model runs continuously and looks for various objects e.g. filler caps. • a second object detection model looks for various defects on the surface of the filler cap Model 1 Model 2
  • 21. Computer Vision with Deep Learning | Andreas Eßbaumer © 2017 Capgemini. All rights reserved. 21 Depending on the number and types of defects the quality check is passed or failed • invariant to different environmental lighting, backgrounds, angles, reflections etc. • Detects small defects like scratches, too much paint or punctual damages. • Even the positions of the defects are given by the model providing. ideal information for potential rework • Built in one day • Uses 100 labelled images per filler cap
  • 22. Computer Vision with Deep Learning | Andreas Eßbaumer Andreas Eßbaumer Senior Data Scientist Capgemini Munich Olof-Palme-Straße 14 81829 Munich andreas.essbaumer@capgemini.com M: +49 151 4025 2219 Summary Convolutional Neural Networks (CNNs) • are the State-of-the-Art for Computer Vision • run via Open-Source-Frameworks • require GPUs and labelled Images • are (relatively) easy to run
  • 23. A global leader in consulting, technology services and digital transformation, Capgemini is at the forefront of innovation to address the entire breadth of clients’ opportunities in the evolving world of cloud, digital and platforms. Building on its strong 50-year heritage and deep industry-specific expertise, Capgemini enables organizations to realize their business ambitions through an array of services from strategy to operations. Capgemini is driven by the conviction that the business value of technology comes from and through people. It is a multicultural company of 200,000 team members in over 40 countries. The Group reported 2016 global revenues of EUR 12.5 billion. About Capgemini Learn more about us at www.capgemini.com This message contains information that may be privileged or confidential and is the property of the Capgemini Group. Copyright © 2017 Capgemini. All rights reserved. People matter, results count.