A data-driven approach to object classification through fog

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.

Department dc:contributor.department

Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2018

Author dc:creator

• Saxena, Alisha

Advisor dc:contributor.advisor

• Ramesh Raskar.

• Electrical Engineering and Computer Science.

Statement dc:rights

• MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

http://hdl.handle.net/1721.1/119716

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/119716