Situational awareness in the maritime environment can be extremely challenging. The maritime environment is highly dynamic and largely undefined, requiring the perception of many potential hazards in the shared maritime environment. One particular challenge is the effect of direct-sunlight exposure and specular reflection causing degradation of camera reliability. It is for this reason then, in this work, the use of High-Dynamic Range imagery for deep learning of semantic image labels is studied in a littoral environment. This study theorizes that the use HDR imagery may be extremely beneficial for the purpose of situational awareness in maritime environments due to the inherent advantages of the technology. This study creates labels for a multi-class semantic segmentation process, and performs well on water and horizon identification in the littoral zone. Additionally, this work contributes proof that water can be reasonably identified using HDR imagery with semantic networks, which is useful for determining the navigable regions for a vessel. This result is a basis on which to build further semantic segmentation work upon in this environment, and could be further improved upon in future works with the introduction of additional data for multi-class segmentation problems.
","abstract_html":"<p>Situational awareness in the maritime environment can be extremely challenging. The maritime environment is highly dynamic and largely undefined, requiring the perception of many potential hazards in the shared maritime environment. One particular challenge is the effect of direct-sunlight exposure and specular reflection causing degradation of camera reliability. It is for this reason then, in this work, the use of High-Dynamic Range imagery for deep learning of semantic image labels is studied in a littoral environment. This study theorizes that the use HDR imagery may be extremely beneficial for the purpose of situational awareness in maritime environments due to the inherent advantages of the technology. This study creates labels for a multi-class semantic segmentation process, and performs well on water and horizon identification in the littoral zone. Additionally, this work contributes proof that water can be reasonably identified using HDR imagery with semantic networks, which is useful for determining the navigable regions for a vessel. This result is a basis on which to build further semantic segmentation work upon in this environment, and could be further improved upon in future works with the introduction of additional data for multi-class segmentation problems.</p>","abstract_has_math":false,"creators":["Montagnoli, Charles"],"institution":null,"degree_name":"Master of Science in Mechanical Engineering","degree_level":"Thesis - Open Access","degree_discipline":"Mechanical Engineering","degree_department":null,"school":null,"contributors":[],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2023,"date_issued":"2023-05-08T07:00:00Z","date_published":"2023-05-08T07:00:00Z","updated_at":"2026-07-27T19:25:52Z","subjects":["Deep learning","maritime environment","camera reliability","HDR imagery","Ocean Engineering","Other Mechanical Engineering"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://commons.erau.edu/edt/735","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:creator","label":"Author","values":["Montagnoli, Charles"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"thesis:degree_discipline","label":"Discipline","values":["Mechanical Engineering"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis - Open Access"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master of Science in Mechanical Engineering"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Deep learning","maritime environment","camera reliability","HDR imagery","Ocean Engineering","Other Mechanical Engineering"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://commons.erau.edu/edt/735"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Situational awareness in the maritime environment can be extremely challenging. The maritime environment is highly dynamic and largely undefined, requiring the perception of many potential hazards in the shared maritime environment. One particular challenge is the effect of direct-sunlight exposure and specular reflection causing degradation of camera reliability. It is for this reason then, in this work, the use of High-Dynamic Range imagery for deep learning of semantic image labels is studied in a littoral environment. This study theorizes that the use HDR imagery may be extremely beneficial for the purpose of situational awareness in maritime environments due to the inherent advantages of the technology. This study creates labels for a multi-class semantic segmentation process, and performs well on water and horizon identification in the littoral zone. Additionally, this work contributes proof that water can be reasonably identified using HDR imagery with semantic networks, which is useful for determining the navigable regions for a vessel. This result is a basis on which to build further semantic segmentation work upon in this environment, and could be further improved upon in future works with the introduction of additional data for multi-class segmentation problems.
"]},{"key":"dc:title","label":"Title","values":["Deep Learning of Semantic Image Labels on HDR Imagery in a Maritime Environment"]}]}],"canonical_facts":{"dc:creator":["Montagnoli, Charles"],"dc:description.abstract":["Situational awareness in the maritime environment can be extremely challenging. The maritime environment is highly dynamic and largely undefined, requiring the perception of many potential hazards in the shared maritime environment. One particular challenge is the effect of direct-sunlight exposure and specular reflection causing degradation of camera reliability. It is for this reason then, in this work, the use of High-Dynamic Range imagery for deep learning of semantic image labels is studied in a littoral environment. This study theorizes that the use HDR imagery may be extremely beneficial for the purpose of situational awareness in maritime environments due to the inherent advantages of the technology. This study creates labels for a multi-class semantic segmentation process, and performs well on water and horizon identification in the littoral zone. Additionally, this work contributes proof that water can be reasonably identified using HDR imagery with semantic networks, which is useful for determining the navigable regions for a vessel. This result is a basis on which to build further semantic segmentation work upon in this environment, and could be further improved upon in future works with the introduction of additional data for multi-class segmentation problems.
"],"dc:identifier":["https://commons.erau.edu/edt/735"],"dc:subject":["Deep learning","maritime environment","camera reliability","HDR imagery","Ocean Engineering","Other Mechanical Engineering"],"dc:title":["Deep Learning of Semantic Image Labels on HDR Imagery in a Maritime Environment"],"thesis:degree_discipline":["Mechanical Engineering"],"thesis:degree_level":["Thesis - Open Access"],"thesis:degree_name":["Master of Science in Mechanical Engineering"]},"updated_at":"2026-07-27T19:25:52Z"}