Quantifying the impact of fog on autonomous driving object detectors and developing a fog-aware vehicle detector

Kore, Ruhi

Permalink

https://hdl.handle.net/2142/120450 Copy

Description

Title

Quantifying the impact of fog on autonomous driving object detectors and developing a fog-aware vehicle detector

Author(s)

Kore, Ruhi

Issue Date

2023-05-04

Director of Research (if dissertation) or Advisor (if thesis)

Forsyth, David

Department of Study

Computer Science

Discipline

Computer Science

Degree Granting Institution

University of Illinois at Urbana-Champaign

Degree Name

M.S.

Degree Level

Thesis

Keyword(s)

• fog
• object
• vehicle
• detector
• detectron
• renderings

Abstract

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.

Graduation Semester

2023-05

Type of Resource

Thesis

Handle URL

https://hdl.handle.net/2142/120450

Copyright and License Information

Copyright 2023 Ruhi Kore

Owning Collections