Every Vessel Counts: Neural Network Based Maritime Traffic Counting System

Monitoring and counting maritime traffic is important for efficient port operations and comprehensive maritime research. However, conventional systems such as the Automatic Identification System (AIS) and Vessel Traffic Services (VTS) often do not provide comprehensive data, especially for the diver...

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Bibliographic Details
Published in:Sensors (Basel, Switzerland) Switzerland), 2023-07, Vol.23 (15), p.6777
Main Authors: Petković, Miro, Vujović, Igor, Kaštelan, Nediljko, Šoda, Joško
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Automation
Cameras
Datasets
Kalman tracker
maritime traffic counting
Monitoring systems
Neural networks
non-AIS vessels
Ports
Sensors
Ships
Surveillance
video surveillance
YOLOv4
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Summary:Monitoring and counting maritime traffic is important for efficient port operations and comprehensive maritime research. However, conventional systems such as the Automatic Identification System (AIS) and Vessel Traffic Services (VTS) often do not provide comprehensive data, especially for the diverse maritime traffic in Mediterranean ports. The paper proposes a real-time vessel counting system using land-based cameras is proposed for maritime traffic monitoring in ports, such as the Port of Split, Croatia. The system consists of a YOLOv4 Convolutional Neural Network (NN), trained and validated on the new SPSCD dataset, that classifies the vessels into 12 categories. Further, the Kalman tracker with Hungarian Assignment (HA) algorithm is used as a multi-target tracker. A stability assessment is proposed to complement the tracking algorithm to reduce false positives by unwanted objects (non-vessels). The evaluation results show that the system has an average counting accuracy of 97.76% and an average processing speed of 31.78 frames per second, highlighting its speed, robustness, and effectiveness. In addition, the proposed system captured 386% more maritime traffic data than conventional AIS systems, highlighting its immense potential for supporting comprehensive maritime research.
ISSN:1424-8220
1424-8220
DOI:10.3390/s23156777