Date of Award
Fall 2025
Access Type
Dissertation - Open Access
Degree Name
Doctor of Philosophy in Mechanical Engineering
Department
Mechanical Engineering
Committee Chair
Eric Coyle
Committee Chair Email
coylee1@erau.edu
Committee Advisor
Eric Coyle
Committee Advisor Email
coylee1@erau.edu
Committee Co-Chair
Subhradeep Roy
Committee Co-Chair Email
ROYS5@erau.edu
First Committee Member
Patrick Currier
First Committee Member Email
currierp@erau.edu
Second Committee Member
Monica R. Garcia
Second Committee Member Email
GARCIM85@erau.edu
Third Committee Member
Jianhua Liu
Third Committee Member Email
liu620@erau.edu
College Dean
James W. Gregory
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.
Scholarly Commons Citation
Lane, Daniel, "A Study in Object Detection and Classification Performance by Sensing Modality for Autonomous Surface Vessels" (2025). Doctoral Dissertations and Master's Theses. 937.
https://commons.erau.edu/edt/937
