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

arXiv: https://arxiv.org/abs/2401.03070 · HTML: https://mlsanity.com/paper/2401.03070/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 70 references that are in the map.
- First posted on arXiv 5 Jan 2024.

## Builds on (most cited first, 13 of 13)

- [SSD: Single Shot MultiBox Detector](https://mlsanity.com/paper/1512.02325/) (2015)
- [Random Erasing Data Augmentation](https://mlsanity.com/paper/1708.04896/) (2017)
- [EfficientDet: Scalable and Efficient Object Detection](https://mlsanity.com/paper/1911.09070/) (2019)
- [An Introduction to Deep Learning for the Physical Layer](https://mlsanity.com/paper/1702.00832/) (2017)
- [Data Augmentation using Random Image Cropping and Patching for Deep CNNs](https://mlsanity.com/paper/1811.09030/) (2018)
- [Pooling Methods in Deep Neural Networks, a Review](https://mlsanity.com/paper/2009.07485/) (2020)
- [A Comprehensive Survey of Deep Transfer Learning for Anomaly Detection in Industrial Time Series: Methods, Applications, and Directions](https://mlsanity.com/paper/2307.05638/) (2023)
- [Using Filter Banks in Convolutional Neural Networks for Texture Classification](https://mlsanity.com/paper/1601.02919/) (2016)
- [Cooperative Semantic Segmentation and Image Restoration in Adverse Environmental Conditions](https://mlsanity.com/paper/1911.00679/) (2019)
- [DeepSeaNet: Improving Underwater Object Detection using EfficientDet](https://mlsanity.com/paper/2306.06075/) (2023)
- [Review on Computer Vision Techniques in Emergency Situation](https://mlsanity.com/paper/1708.07455/) (2017)
- [Moving Vehicle Detection Using AdaBoost and Haar-Like Feature in Surveillance Videos](https://mlsanity.com/paper/1801.01698/) (2018)
- [Real-Time Helmet Violation Detection Using YOLOv5 and Ensemble Learning](https://mlsanity.com/paper/2304.09246/) (2023)

## Built on by (most cited first, 2 of 2)

- [Predicting Barge Presence and Quantity on Inland Waterways using Vessel Tracking Data: A Machine Learning Approach](https://mlsanity.com/paper/2501.00615/) (2024)
- [Predicting Barge Tow Size on Inland Waterways Using Vessel Trajectory Derived Features: Proof of Concept](https://mlsanity.com/paper/2510.23994/) (2025)

## Abstract (arXiv)

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.