Object detection through computer vision has traditionally been difficult to reliably implement due to various lighting conditions caused by weather and time of day. Any changes in conditions can be detrimental to the detector’s ability to accurately identify objects. A modern approach implements deep learning techniques to classify and train a neural network. While highly effective, this approach can be cumbersome and computationally intensive. This project will investigate the feasibility of using deep learning to detect, classify, and track objects in near real-time while being processed on a mobile platform. I will investigate the feasibility of these processes on a small embedded system, such as the NVIDIA Jetson TX1. I will investigate several promising algorithms such as Faster R-CNN, TensorBox, DetectNet, and YOLO. This research is beneficial because it will transition deep learning techniques developed primarily for research in a lab environment to a real-world situation in which high accuracy and fast processing are vital. The work solved through this research will greatly benefit platforms that require object detection capabilities, but do not have the space, budget, or power capabilities for large GPUs or GPU clusters.
","abstract_html":"<p>Object detection through computer vision has traditionally been difficult to reliably implement due to various lighting conditions caused by weather and time of day. Any changes in conditions can be detrimental to the detector’s ability to accurately identify objects. A modern approach implements deep learning techniques to classify and train a neural network. While highly effective, this approach can be cumbersome and computationally intensive. This project will investigate the feasibility of using deep learning to detect, classify, and track objects in near real-time while being processed on a mobile platform. I will investigate the feasibility of these processes on a small embedded system, such as the NVIDIA Jetson TX1. I will investigate several promising algorithms such as Faster R-CNN, TensorBox, DetectNet, and YOLO. This research is beneficial because it will transition deep learning techniques developed primarily for research in a lab environment to a real-world situation in which high accuracy and fast processing are vital. The work solved through this research will greatly benefit platforms that require object detection capabilities, but do not have the space, budget, or power capabilities for large GPUs or GPU clusters.</p>","abstract_has_math":false,"creators":["Goring, Robert"],"institution":null,"degree_name":"Master of Science in Electrical & Computer Engineering","degree_level":"Thesis - Open Access","degree_discipline":"Electrical, Computer, Software, and Systems Engineering","degree_department":null,"school":null,"contributors":[],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2017,"date_issued":"2017-04-01T07:00:00Z","date_published":"2017-04-01T07:00:00Z","updated_at":"2026-07-27T19:26:28Z","subjects":["neural networks","maritime","visual detection","mobile platform","Electrical and Computer Engineering","Other Electrical and Computer Engineering"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://commons.erau.edu/edt/331","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:creator","label":"Author","values":["Goring, Robert"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"thesis:degree_discipline","label":"Discipline","values":["Electrical, Computer, Software, and Systems Engineering"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis - Open Access"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master of Science in Electrical & Computer Engineering"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["neural networks","maritime","visual detection","mobile platform","Electrical and Computer Engineering","Other Electrical and Computer Engineering"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://commons.erau.edu/edt/331"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Object detection through computer vision has traditionally been difficult to reliably implement due to various lighting conditions caused by weather and time of day. Any changes in conditions can be detrimental to the detector’s ability to accurately identify objects. A modern approach implements deep learning techniques to classify and train a neural network. While highly effective, this approach can be cumbersome and computationally intensive. This project will investigate the feasibility of using deep learning to detect, classify, and track objects in near real-time while being processed on a mobile platform. I will investigate the feasibility of these processes on a small embedded system, such as the NVIDIA Jetson TX1. I will investigate several promising algorithms such as Faster R-CNN, TensorBox, DetectNet, and YOLO. This research is beneficial because it will transition deep learning techniques developed primarily for research in a lab environment to a real-world situation in which high accuracy and fast processing are vital. The work solved through this research will greatly benefit platforms that require object detection capabilities, but do not have the space, budget, or power capabilities for large GPUs or GPU clusters.
"]},{"key":"dc:title","label":"Title","values":["Feasibility of Neural Networks for Maritime Visual Detection on a Mobile Platform"]}]}],"canonical_facts":{"dc:creator":["Goring, Robert"],"dc:description.abstract":["Object detection through computer vision has traditionally been difficult to reliably implement due to various lighting conditions caused by weather and time of day. Any changes in conditions can be detrimental to the detector’s ability to accurately identify objects. A modern approach implements deep learning techniques to classify and train a neural network. While highly effective, this approach can be cumbersome and computationally intensive. This project will investigate the feasibility of using deep learning to detect, classify, and track objects in near real-time while being processed on a mobile platform. I will investigate the feasibility of these processes on a small embedded system, such as the NVIDIA Jetson TX1. I will investigate several promising algorithms such as Faster R-CNN, TensorBox, DetectNet, and YOLO. This research is beneficial because it will transition deep learning techniques developed primarily for research in a lab environment to a real-world situation in which high accuracy and fast processing are vital. The work solved through this research will greatly benefit platforms that require object detection capabilities, but do not have the space, budget, or power capabilities for large GPUs or GPU clusters.
"],"dc:identifier":["https://commons.erau.edu/edt/331"],"dc:subject":["neural networks","maritime","visual detection","mobile platform","Electrical and Computer Engineering","Other Electrical and Computer Engineering"],"dc:title":["Feasibility of Neural Networks for Maritime Visual Detection on a Mobile Platform"],"thesis:degree_discipline":["Electrical, Computer, Software, and Systems Engineering"],"thesis:degree_level":["Thesis - Open Access"],"thesis:degree_name":["Master of Science in Electrical & Computer Engineering"]},"updated_at":"2026-07-27T19:26:28Z"}