Date of Award
Spring 2023
Access Type
Thesis - Open Access
Degree Name
Master of Science in Mechanical Engineering
Department
Mechanical Engineering
Committee Chair
Eric Coyle
First Committee Member
Patrick Currier
Second Committee Member
Jianhua Liu
College Dean
James W. Gregory
Abstract
The field of autonomous robotics has benefited from the implementation of convolutional neural networks in vision-based situational awareness. These strategies help identify surface obstacles and nearby vessels. This study proposes the introduction of high dynamic range cameras on autonomous surface vessels because these cameras capture images at different levels of exposure revealing more detail than fixed exposure cameras. To see if this introduction will be beneficial for autonomous vessels this research will create a dataset of labeled high dynamic range images and single exposure images, then train object detection networks with these datasets to compare the performance of these networks. Faster-RCNN, SSD, and YOLOv5 were used to compare. Results determined Faster-RCNN and YOLOv5 networks trained on fixed exposure images outperformed their HDR counterparts while SSDs performed better when using HDR images. Better fixed exposure network performance is likely attributed to better feature extraction for fixed exposure images. Despite performance metrics, HDR images prove more beneficial in cases of extreme light exposure since features are not lost.
Scholarly Commons Citation
Landaeta, Erasmo, "Assessing High Dynamic Range Imagery Performance for Object Detection in Maritime Environments" (2023). Doctoral Dissertations and Master's Theses. 730.
https://commons.erau.edu/edt/730
