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.

使用现有的交通摄像头和深度学习模型，本研究开发了一种检测内陆水路驳船交通的方法，并可用于匿名的大宗商品追踪和监测，为公共交通机构的长期交通规划以及美国工程兵团等联邦机构的运营和维护规划提供有价值的数据。

交通摄像头用于检测内陆水路驳船交通：机器学习应用

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

This paper proposes a realistic modularized framework for controlling
autonomous surface vehicles (ASVs) on inland waterways (IWs) based on deep
reinforcement learning (DRL). The framework comprises two levels: a high-level
local path planning (LPP) unit and a low-level path following (PF) unit, each
consisting of a DRL agent. The LPP agent is responsible for planning a path
under consideration of nearby vessels, traffic rules, and the geometry of the
waterway. We thereby leverage a recently proposed spatial-temporal recurrent
neural network architecture, which is transferred to continuous action spaces.
The PF agent is responsible for low-level actuator control while accounting for
shallow water influences on the marine craft and the environmental forces
winds, waves, and currents. Both agents are thoroughly validated in simulation,
employing the lower Elbe in northern Germany as an example case and using real
AIS trajectories to model the behavior of other ships.

本文提出了一个基于深度强化学习的逼真的模块化框架，用于控制内陆水路上的自主表面船舶（ASV）。该框架包括两个层次：高级本地路径规划（LPP）单元和低级路径跟踪（PF）单元，每个单元都包含一个 DRL 代理。其中，LPP 代理负责考虑附近船只、交通规则和水路几何形状来规划路径，我们利用了最近提出的空间时间循环神经网络架构，在连续行动空间下进行转移。PF 代理负责低级执行器控制，同时考虑浅水对海船的影响以及环境力量如风、浪和洋流。两个代理在仿真环境中进行了全面验证，以德国北部的下艾尔伯为案例，使用真实的 AIS 轨迹来模拟其他船只的行为。