用于检测内河驳船交通的交通摄像头：一项机器学习应用
Traffic Cameras to detect inland waterway barge traffic: An Application of machine learning
AI总结：
针对内河驳船货运流量缺乏实时监测手段的问题，本研究利用现有交通摄像头结合YOLOv8等深度学习模型实现驳船检测，在密西西比河与俄亥俄河数据集上取得96%的F1值，可支撑交通与运维规划。
摘要：
内河航道对货运至关重要，但目前监测货运船舶（如驳船）在内河航道的运行情况与使用程度的手段十分有限。尽管拖船、顶推船等船舶的追踪方法可通过自动识别系统（AIS）公开获取，但追踪这些关键海运通道上驳船运载的货运吨位和商品流向的方法仍属空白，实时场景下的相关技术更是缺失。本文提出一种利用现有视角合适的交通摄像头检测内河航道驳船交通的方法。研究采用了深度学习模型，具体包括You Only Look Once（YOLO，你只看一次）、Single Shot MultiBox Detector（SSD，单阶段多框检测器）和EfficientDet（高效检测）模型。该模型可从视频中检测船舶和/或驳船的存在，并执行分类任务，类别分为：无船舶或驳船、未拖带驳船的船舶、拖带驳船的船舶、驳船。研究从密西西比河和俄亥俄河沿岸的5个现有交通摄像头收集了包含331张标注图像的数据集用于模型开发。其中YOLOv8的F1-score达到96%，性能优于F1-score分别为86%、79%、77%的YOLOv5、SSD和EfficientDet模型。研究还针对天气条件（雾和雨）和地点（密西西比河和俄亥俄河）开展了敏感性分析；在地点敏感性分析中，采用背景减除技术对不同地点的视频图像进行归一化处理。该模型可用于检测河段沿线的驳船存在情况，可用于匿名大宗商品追踪与监测。这类数据除了供美国陆军工程兵团等联邦机构开展运营与维护规划外，对公共交通机构开展的远期交通规划工作也具有重要价值。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
