Date of Award

Spring 2023

Access Type

Thesis - Open Access

Degree Name

Master of Science in Mechanical Engineering


Mechanical Engineering

Committee Chair

Eric Coyle

First Committee Member

Patrick Currier

Second Committee Member

Jianhua Liu

College Dean

Jim Gregory


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.