This research presents a quantitative performance comparison between light detection and ranging (LiDAR) and vision-based sensing for real-time maritime object detection on autonomous surface vessels. Using Embry-Riddle Aeronautical University’s (ERAU) Minion platform and 2024 Maritime RobotX Challenge data, this study evaluates the detection of six maritime object categories using two representative models. YOLOv8 provides a neural network vision-based method, and GB-CACHE provides a deterministic LiDAR-based method. Both models have been previously demonstrated to run in real time on uncrewed surface vessels (USVs). The evaluation methodology encompasses multi-sensor calibration, real-time performance analysis, and the introduction of a late-fusion strategy in the image frame to reduce bounding-box uncertainty. Performance metrics include training requirements, precision, recall, mean average precision (mAP), and computational efficiency. YOLO achieves low-latency, high-mAP visual detection, while GB-CACHE provides deterministic, CPU-level runtime guarantees with high geometric accuracy, and the Kalman weighted fusion guarantees an improvement in the bounds of the region of interest.
","abstract_html":"<p>This research presents a quantitative performance comparison between light detection and ranging (LiDAR) and vision-based sensing for real-time maritime object detection on autonomous surface vessels. Using Embry-Riddle Aeronautical University’s (ERAU) Minion platform and 2024 Maritime RobotX Challenge data, this study evaluates the detection of six maritime object categories using two representative models. YOLOv8 provides a neural network vision-based method, and GB-CACHE provides a deterministic LiDAR-based method. Both models have been previously demonstrated to run in real time on uncrewed surface vessels (USVs). The evaluation methodology encompasses multi-sensor calibration, real-time performance analysis, and the introduction of a late-fusion strategy in the image frame to reduce bounding-box uncertainty. Performance metrics include training requirements, precision, recall, mean average precision (mAP), and computational efficiency. YOLO achieves low-latency, high-mAP visual detection, while GB-CACHE provides deterministic, CPU-level runtime guarantees with high geometric accuracy, and the Kalman weighted fusion guarantees an improvement in the bounds of the region of interest.</p>","abstract_has_math":false,"creators":["Lane, Daniel"],"institution":null,"degree_name":"Doctor of Philosophy in Mechanical Engineering","degree_level":"Dissertation - Open Access","degree_discipline":"Mechanical Engineering","degree_department":null,"school":null,"contributors":[],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2025,"date_issued":"2025-10-01T07:00:00Z","date_published":"2025-10-01T07:00:00Z","updated_at":"2026-07-27T19:26:22Z","subjects":["Real-Time; Sensor; Fusion; Maritime; Perception","Robotics"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://commons.erau.edu/edt/937","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:creator","label":"Author","values":["Lane, Daniel"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"thesis:degree_discipline","label":"Discipline","values":["Mechanical Engineering"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Dissertation - Open Access"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Doctor of Philosophy in Mechanical Engineering"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Real-Time; Sensor; Fusion; Maritime; Perception","Robotics"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://commons.erau.edu/edt/937"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["This research presents a quantitative performance comparison between light detection and ranging (LiDAR) and vision-based sensing for real-time maritime object detection on autonomous surface vessels. Using Embry-Riddle Aeronautical University’s (ERAU) Minion platform and 2024 Maritime RobotX Challenge data, this study evaluates the detection of six maritime object categories using two representative models. YOLOv8 provides a neural network vision-based method, and GB-CACHE provides a deterministic LiDAR-based method. Both models have been previously demonstrated to run in real time on uncrewed surface vessels (USVs). The evaluation methodology encompasses multi-sensor calibration, real-time performance analysis, and the introduction of a late-fusion strategy in the image frame to reduce bounding-box uncertainty. Performance metrics include training requirements, precision, recall, mean average precision (mAP), and computational efficiency. YOLO achieves low-latency, high-mAP visual detection, while GB-CACHE provides deterministic, CPU-level runtime guarantees with high geometric accuracy, and the Kalman weighted fusion guarantees an improvement in the bounds of the region of interest.
"]},{"key":"dc:title","label":"Title","values":["A Study in Object Detection and Classification Performance by Sensing Modality for Autonomous Surface Vessels"]}]}],"canonical_facts":{"dc:creator":["Lane, Daniel"],"dc:description.abstract":["This research presents a quantitative performance comparison between light detection and ranging (LiDAR) and vision-based sensing for real-time maritime object detection on autonomous surface vessels. Using Embry-Riddle Aeronautical University’s (ERAU) Minion platform and 2024 Maritime RobotX Challenge data, this study evaluates the detection of six maritime object categories using two representative models. YOLOv8 provides a neural network vision-based method, and GB-CACHE provides a deterministic LiDAR-based method. Both models have been previously demonstrated to run in real time on uncrewed surface vessels (USVs). The evaluation methodology encompasses multi-sensor calibration, real-time performance analysis, and the introduction of a late-fusion strategy in the image frame to reduce bounding-box uncertainty. Performance metrics include training requirements, precision, recall, mean average precision (mAP), and computational efficiency. YOLO achieves low-latency, high-mAP visual detection, while GB-CACHE provides deterministic, CPU-level runtime guarantees with high geometric accuracy, and the Kalman weighted fusion guarantees an improvement in the bounds of the region of interest.
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