{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/120450"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/120450","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Quantifying the impact of fog on autonomous driving object detectors and developing a fog-aware vehicle detector","abstract":"Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2025-05-01","abstract_html":"Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2025-05-01","abstract_has_math":false,"creators":["Kore, Ruhi"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Computer Science","degree_department":null,"school":null,"contributors":["Forsyth, David"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2023,"date_issued":"2023-05","date_published":"2023-05","updated_at":"2026-07-22T22:24:57Z","subjects":["Fog","Object","Vehicle","Detector","Detectron","Renderings"],"languages":["en","eng"],"rights":["Copyright 2023 Ruhi Kore"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/2142/120450","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Forsyth, David"]},{"key":"dc:creator","label":"Author","values":["Kore, Ruhi"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2023-05","2023-05-04"]},{"key":"dc:type","label":"Dc Type","values":["text"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Computer Science"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["M.S."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois at Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Fog","Object","Vehicle","Detector","Detectron","Renderings"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en","eng"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2023 Ruhi Kore"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://hdl.handle.net/2142/120450"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2025-05-01","The student, Ruhi Kore, accepted the attached license on 2023-05-02 at 17:59.","The student, Ruhi Kore, submitted this Thesis for approval on 2023-05-02 at 18:12.","This Thesis was approved for publication on 2023-05-04 at 09:49.","DSpace SAF Submission Ingestion Package generated from Vireo submission #19282 on 2023-09-01 at 17:15:47","An essential component of developing robust autonomous driving software is the ability to successfully navigate in various weather conditions, such as in fog, rain, and snow. Autonomous vehicles today rely on hardware (e.g., cameras) and sensors (e.g., lidar, radar) to understand their environment so they can navigate their surroundings accordingly. However, severe weather impacts the quality of data obtained by the hardware and sensors. For instance, in foggy weather conditions, the contrast in the images obtained by cameras drops significantly, making it difficult for intelligent image processing algorithms to perform object detection and image classification. In this project, we quantify the impact of fog on the accuracy and confidence levels of current state-of-the-art object detectors, focusing on the task of identifying other vehicles on the road in foggy weather conditions. We do so by curating a dataset of road images from the driver’s perspective, with various levels of fog synthetically added to each image. We also design a vehicle detector that can identify vehicles in fog with a higher accuracy and confidence level compared to current state-of-the-art detectors. Our fine-tuned detector’s persistence in correctly identifying vehicles is, on average, 4.69% higher in light fog, 13.38% higher in medium-intensity fog, and 23.65% higher in heavy fog."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Quantifying the impact of fog on autonomous driving object detectors and developing a fog-aware vehicle detector"]}]}],"canonical_facts":{"dc:contributor":["Forsyth, David"],"dc:creator":["Kore, Ruhi"],"dc:date":["2023-05","2023-05-04"],"dc:description":["Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2025-05-01","The student, Ruhi Kore, accepted the attached license on 2023-05-02 at 17:59.","The student, Ruhi Kore, submitted this Thesis for approval on 2023-05-02 at 18:12.","This Thesis was approved for publication on 2023-05-04 at 09:49.","DSpace SAF Submission Ingestion Package generated from Vireo submission #19282 on 2023-09-01 at 17:15:47","An essential component of developing robust autonomous driving software is the ability to successfully navigate in various weather conditions, such as in fog, rain, and snow. Autonomous vehicles today rely on hardware (e.g., cameras) and sensors (e.g., lidar, radar) to understand their environment so they can navigate their surroundings accordingly. However, severe weather impacts the quality of data obtained by the hardware and sensors. For instance, in foggy weather conditions, the contrast in the images obtained by cameras drops significantly, making it difficult for intelligent image processing algorithms to perform object detection and image classification. In this project, we quantify the impact of fog on the accuracy and confidence levels of current state-of-the-art object detectors, focusing on the task of identifying other vehicles on the road in foggy weather conditions. We do so by curating a dataset of road images from the driver’s perspective, with various levels of fog synthetically added to each image. We also design a vehicle detector that can identify vehicles in fog with a higher accuracy and confidence level compared to current state-of-the-art detectors. Our fine-tuned detector’s persistence in correctly identifying vehicles is, on average, 4.69% higher in light fog, 13.38% higher in medium-intensity fog, and 23.65% higher in heavy fog."],"dc:format":["application/pdf"],"dc:identifier":["https://hdl.handle.net/2142/120450"],"dc:language":["en","eng"],"dc:rights":["Copyright 2023 Ruhi Kore"],"dc:subject":["Fog","Object","Vehicle","Detector","Detectron","Renderings"],"dc:title":["Quantifying the impact of fog on autonomous driving object detectors and developing a fog-aware vehicle detector"],"dc:type":["text"],"thesis:degree_discipline":["Computer Science"],"thesis:degree_level":["Thesis"],"thesis:degree_name":["M.S."],"thesis:institution_name":["University of Illinois at Urbana-Champaign"]},"updated_at":"2026-07-22T22:24:57Z"}