Date of Graduation
8-2024
Document Type
Thesis
Degree Name
Master of Science in Civil Engineering (MSCE)
Degree Level
Graduate
Department
Civil Engineering
Advisor/Mentor
Hernandez, Sarah
Committee Member
Mitra, Suman
Second Committee Member
Poddar, Subhadipto
Keywords
Automatic Identification System (AIS); Barge operations; Detection and identification systems; Inland waterways; Machine learning; Marine safety
Abstract
Inland waterways are critical for freight movement, but limited means exist for monitoring their performance and usage by freight-carrying vessels, e.g., barges. While methods to track vessels, e.g., tug and tow boats, are publicly available through Automatic Identification Systems (AIS), ways to track freight tonnages and commodity flows carried on barges along these critical marine highways are non-existent, especially in real-time settings. This paper develops a method to detect barge traffic on inland waterways using existing traffic cameras with opportune viewing angles. Deep learning models, specifically, You Only Look Once (YOLO), Single Shot MultiBox Detector (SSD), and EfficientDet are employed. The model detects the presence of vessels and/or barges from video and performs a classification (no vessel or barge, vessel without barge, vessel with barge, and barge). A dataset of 331 annotated images was collected from five existing traffic cameras along the Mississippi and Ohio Rivers for model development. YOLOv8 achieves an F1-score of 96%, outperforming YOLOv5, SSD, and EfficientDet models with 86%, 79%, and 77% respectively. Sensitivity analysis was carried out regarding weather conditions (fog and rain) and location (Mississippi and Ohio rivers). A background subtraction technique was used to normalize video images across the various locations for the location sensitivity analysis. This model can be used to detect the presence of barges along river segments, which can be used for anonymous bulk commodity tracking and monitoring. Such data is valuable for long-range transportation planning efforts carried out by public transportation agencies, in addition to operational and maintenance planning conducted by federal agencies such as the US Army Corp of Engineers
Citation
Agorku, G. E. (2024). Real-Time Barge Detection Using Traffic Cameras and Deep Learning on Inland Waterways. Graduate Theses and Dissertations Retrieved from https://scholarworks.uark.edu/etd/5454