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Inland waterways are critical for freight movement, but limited means existfor monitoring their performance and usage by freight-carrying vessels, e.g.,barges. While methods to track vessels, e.g., tug and tow boats, are publiclyavailable through Automatic Identification Systems (AIS), ways to track freighttonnages and commodity flows carried on barges along these critical marinehighways are non-existent, especially in real-time settings. This paperdevelops a method to detect barge traffic on inland waterways using existingtraffic 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/orbarges from video and performs a classification (no vessel or barge, vesselwithout barge, vessel with barge, and barge). A dataset of 331 annotated imageswas collected from five existing traffic cameras along the Mississippi and OhioRivers for model development. YOLOv8 achieves an F1-score of 96%, outperformingYOLOv5, SSD, and EfficientDet models with 86%, 79%, and 77% respectively.Sensitivity analysis was carried out regarding weather conditions (fog andrain) and location (Mississippi and Ohio rivers). A background subtractiontechnique was used to normalize video images across the various locations forthe location sensitivity analysis. This model can be used to detect thepresence of barges along river segments, which can be used for anonymous bulkcommodity tracking and monitoring. Such data is valuable for long-rangetransportation planning efforts carried out by public transportation agencies,in addition to operational and maintenance planning conducted by federalagencies such as the US Army Corp of Engineers.

Traffic Cameras to detect inland waterway barge traffic: An Application of machine learning