{"id":{"repo_id":"mit","oai_identifier":"oai:dspace.mit.edu:1721.1/119716"},"canonical_url":"https://search.dev.ndltd.org/etd/mit/oai:dspace.mit.edu:1721.1/119716","repository":{"repo_id":"mit","name":"MIT","base_url":"https://dspace.mit.edu/oai/request"},"display":{"title":"A data-driven approach to object classification through fog","abstract":"Identifying objects through fog is an important problem that is difficult even for the human eye. Solving this problem would make autonomous vehicles, drones, and other similar systems more resilient to changing natural weather conditions. While there are existing solutions for dehazing images occluded by light fog, these solutions are not effective in cases of very dense fog. Hence, we present a system that uses a combination of time resolved sensing, specifically using Single Photon Avalanche Photodiode (SPAD) cameras, and deep learning with convolutional neural networks to detect and classify objects when imaged through extreme scattering media like fog. This thesis describes our three-pronged approach to solving this problem: (1) building simulation software to gather sufficient training data, (2) verifying and benchmarking output of simulation with real-life fog data, (3) training deep learning models to classify objects occluded by fog.","abstract_html":"Identifying objects through fog is an important problem that is difficult even for the human eye. Solving this problem would make autonomous vehicles, drones, and other similar systems more resilient to changing natural weather conditions. While there are existing solutions for dehazing images occluded by light fog, these solutions are not effective in cases of very dense fog. Hence, we present a system that uses a combination of time resolved sensing, specifically using Single Photon Avalanche Photodiode (SPAD) cameras, and deep learning with convolutional neural networks to detect and classify objects when imaged through extreme scattering media like fog. This thesis describes our three-pronged approach to solving this problem: (1) building simulation software to gather sufficient training data, (2) verifying and benchmarking output of simulation with real-life fog data, (3) training deep learning models to classify objects occluded by fog.","abstract_has_math":false,"creators":["Saxena, Alisha"],"institution":"Massachusetts Institute of Technology","degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":"Massachusetts Institute of Technology. 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The certified thesis is available in the Institute Archives and Special Collections.","Cataloged from student-submitted PDF version of thesis.","Includes bibliographical references (page 51)."]},{"key":"dc:description.abstract","label":"Abstract","values":["Identifying objects through fog is an important problem that is difficult even for the human eye. Solving this problem would make autonomous vehicles, drones, and other similar systems more resilient to changing natural weather conditions. While there are existing solutions for dehazing images occluded by light fog, these solutions are not effective in cases of very dense fog. Hence, we present a system that uses a combination of time resolved sensing, specifically using Single Photon Avalanche Photodiode (SPAD) cameras, and deep learning with convolutional neural networks to detect and classify objects when imaged through extreme scattering media like fog. This thesis describes our three-pronged approach to solving this problem: (1) building simulation software to gather sufficient training data, (2) verifying and benchmarking output of simulation with real-life fog data, (3) training deep learning models to classify objects occluded by fog."]},{"key":"dc:description.degree","label":"Dc Description Degree","values":["M. Eng."]},{"key":"dc:title","label":"Title","values":["A data-driven approach to object classification through fog"]}]}],"canonical_facts":{"dc:contributor.advisor":["Ramesh Raskar."],"dc:contributor.department":["Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science."],"dc:contributor.other":["Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science."],"dc:creator":["Saxena, Alisha"],"dc:date.accessioned":["2018-12-18T19:47:01Z"],"dc:date.available":["2018-12-18T19:47:01Z"],"dc:date.issued":["2018"],"dc:description":["Thesis: M. 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Hence, we present a system that uses a combination of time resolved sensing, specifically using Single Photon Avalanche Photodiode (SPAD) cameras, and deep learning with convolutional neural networks to detect and classify objects when imaged through extreme scattering media like fog. This thesis describes our three-pronged approach to solving this problem: (1) building simulation software to gather sufficient training data, (2) verifying and benchmarking output of simulation with real-life fog data, (3) training deep learning models to classify objects occluded by fog."],"dc:description.degree":["M. Eng."],"dc:identifier.uri":["http://hdl.handle.net/1721.1/119716"],"dc:language.iso":["eng"],"dc:publisher":["Massachusetts Institute of Technology"],"dc:rights":["MIT theses are protected by copyright. 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