Object Detection in Visually Degraded Environments
Team 29: Dustin Barnes, Jack Currie, Brenda Penn
CS 426 Senior Project in Computer Science
Spring 2018
CSE Department
CS 426 Senior Project in Computer Science
Spring 2018
CSE Department
Currently, autonomous vehicles have the ability to
function in ideal environments, for example on a clear and sunny day.
However, in visually degraded environments, these vehicles have a much
more difficult time localizing themselves and detecting objects on or
adjacent to the roadway. When a car is operating in low-light conditions
or in inclement weather, the current computer vision modules that the
cars use to navigate have difficulty detecting objects in their
surroundings. A solution has yet to be found to successfully navigate
these environments in real time without reliance on additional LiDAR and
GPS sensors. For our capstone project, we seek to make a contribution to
the problem of object detection in dark environments. We will approach
this by gathering video data from a vehicle operating at night time and
training an object classifier to detect and locate vehicles in these
frames. Using PointGrey cameras, our intent is to collect the data using
the Robot Operating System (ROS), and then train an object classifier
for vehicles in these conditions. We will use the open source Python
framework TensorFlow to construct the neural network and train our image
classifier.
With the ability for an autonomous vehicle to detect features in these
varying conditions, it can become a commercially viable consumer product
as it would permit end-users to operate autonomous vehicles in the dark,
when it is raining, or when it is snowing. In addition, this feature
would provide users with a higher guaranteed level of safety.
Advisor
Assistant Professor
University of Nevada, Reno
Instructor
Professor
University of Nevada, Reno
Instructor
Visiting Lecturer
University of Nevada, Reno