  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.
