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COMPUTER VISION and BUILDING ENVELOPES a Thesis Submitted To COMPUTER VISION AND BUILDING ENVELOPES A thesis submitted To Kent State University in partial Fulfillment of the requirements for the Degree of Master of Science in Architecture and Environmental Design by Nina K. Anani-Manyo April 2021 © Copyright All rights reserved Except for previously published materials Thesis written by Nina K. Anani-Manyo B.S. Architecture, Kent State University, 2019 M. Architecture, Kent State University, 2021 M. S., Kent State University, 2021 Approved by Rui Liu, Ph.D. __________________________ , Advisor Reid Coffman, Ph.D. __________________________ , Program Coordinator Ivan Bernal __________________________ , Director, Architecture and Urban Design Mark Mistur, AIA, __________________________ , Dean, College of Architecture and Environmental Design ABSTRACT Computer vision, a field that falls under artificial intelligence (AI), is increasingly establishing grounds in many disciplines as the demand for automated means to solve real-world problems gradually grows. AI is progressively simplifying and speeding up the processes of day-to-day tasks. The application of computer vision within the field of architecture has the potential to increase efficiency as well. Building envelope is an important component of a building and requires regular assessment and inspection. The application of deep learning techniques reveals itself as an innovative way of carrying out a task that is typically performed by humans. Hence, this research discusses the explorations of using computer vision as a tool to classify building materials, evaluate the details, and potentially identify distresses of building envelopes. This is done using a collection of existing digital images and algorithms that help train the computer to produce efficient and reliable results. Deep learning techniques such as convolutional neural network algorithms and Google’s Teachable Machine are utilized to classify two sets of base data. The successes produced prove the models have the capability of classifying the dataset given to them. These approaches gradually introduce new methods and techniques that can and will revolutionize the industry of Architecture, Engineering, and Construction. Keywords: Computer vision, architecture, building envelope, deep learning, algorithm, convolutional neural network (CNN), image classification. TABLE OF CONTENTS LIST OF FIGURES ............................................................................................................................. vii ACKNOWLEDGEMENTS .................................................................................................................... x CHAPTER 1 INTRODUCTION AND BACKGROUND ........................................................................... 1 1.1 Background ............................................................................................................................ 1 1.2 Artificial Intelligence .............................................................................................................. 5 1.3 Computer Vision .................................................................................................................... 7 1.4 Problem Statement ............................................................................................................. 11 1.5 Research Objective and Questions ..................................................................................... 12 1.6 Thesis Outline ...................................................................................................................... 13 CHAPTER 2 LITERATURE REVIEW .................................................................................................. 14 2.1 History of Computer Vision ................................................................................................. 14 2.2 Images ................................................................................................................................. 17 2.3 Deep Learning ..................................................................................................................... 20 2.3.1 Introduction .................................................................................................................. 20 2.3.2 Activation Functions ..................................................................................................... 22 2.3.3 Deep Learning Algorithms ............................................................................................ 26 2.3.4 Tools and Open-Source Software ................................................................................. 29 iv 2.4 Design Application ............................................................................................................... 30 2.4.1 Computer Vision and Art .............................................................................................. 30 2.4.2 Computer Vision and Building and Infrastructure Performance .................................. 35 2.4.3 Computer Vision and Urban Design ............................................................................. 39 2.5 Summary ............................................................................................................................. 41 CHAPTER 3: CNN AND TEACHABLE MACHINE .............................................................................. 43 3.1 Introduction ......................................................................................................................... 43 3.2 CNN ...................................................................................................................................... 43 3.3 Teachable Machine ............................................................................................................. 45 3.3.1 ImageNet ...................................................................................................................... 47 3.4 Summary ............................................................................................................................. 48 CHAPTER 4: DATA, RESULTS AND DISCUSSIONS ........................................................................... 49 4.1 Data ..................................................................................................................................... 49 4.1.1 Data Collection ............................................................................................................. 50 4.1.2 Data Processing ............................................................................................................ 53 4.2 CNN ...................................................................................................................................... 54 4.2.1 Classifications for Roof Materials ................................................................................. 54 4.2.2 Classifications of Damaged or Not Damaged Roof ....................................................... 60 4.3 Teachable Machine ............................................................................................................. 64 v 4.3.1 Classifications for Roof Materials ................................................................................. 64 4.3.2 Classifications of Damaged or Not Damaged Roof ....................................................... 66 4.4 Results, Comparisons, and Limitations ............................................................................... 67 4.5 Summary ............................................................................................................................. 71 CHAPTER 5: CONCLUSION ............................................................................................................. 73 5.1 Conclusions .......................................................................................................................... 73 5.2 Overall Limitations .............................................................................................................. 73 5.3 Recommendations .............................................................................................................. 74 REFERENCES .................................................................................................................................. 76 APPENDICES .................................................................................................................................. 82 Appendix A - Python Code for Downloading Images ................................................................ 83 Appendix B- CNN Code for Roof Material Image Classification ................................................ 85 vi LIST OF FIGURES Figure 1-1: Turing Test…………………………………………………………………............................................... 2 Figure 1-2: Timeline of Computer Vision-Artificial Intelligence ………............................................ 4 Figure 1-3: Different Brick Wall Properties ………………….…………………............................................ 9 Figure 2-1: Visual Turing Test.………………………………………………………….......................................... 16 Figure 2-2: Simple “Display Image” Code…………………………………………......................................... 18 Figure 2-3: Images…………………………………………………………………………........................................... 19 Figure 2-4: An Artificial Neuron……………………………………………………….......................................... 21 Figure 2-5: Activation Function……………………………………………………….......................................... 22 Figure 2-6: Equation, Range and Derivative……………………………………….......................................
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