Boat Detection System in the Chao Phraya River Using YOLOv7 Techniques

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Published: Jun 30, 2025
Keywords:
Boat Detection Chao Phraya River YOLOv7 water traffic performance

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Sarawut Saitawee
Engineering Program in Defence Engineering, School of Engineering, King Mongkut’s Institute of Technology Ladkrabang
Somchat Jiriwibhakorn
Department of Electrical Engineering, School of Engineering, King Mongkut’s Institute of Technology Ladkrabang

Abstract

This research aims to develop a system for detecting boats in the Chao Phraya River using the YOLOv7 technique to address the issues of heavy and hazardous water traffic. The performance of three algorithms YOLO (You Only Look Once), SSD (Single Shot MultiBox Detector), and Faster R-CNN (Region-based Convolutional Neural Network) was compared using a dataset of 1,000 images from cameras with a resolution of 608x608 pixels, consisting of 750 images with boats and 250 images without boats. The models' performance was evaluated using a confusion matrix. The test results showed that the YOLOv7 algorithm achieved the highest F1-Score (95.0%), followed by Faster R-CNN (93.0%) and SSD (82.0%). Therefore, the YOLOv7 algorithm was the most effective in detecting boats in the Chao Phraya River in this experiment.

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How to Cite
[1]
S. Saitawee and S. Jiriwibhakorn, “Boat Detection System in the Chao Phraya River Using YOLOv7 Techniques”, sej, vol. 21, no. 1, pp. 77–91, Jun. 2025.
Issue
Vol. 21 No. 1 (2025): January - June 2025
Section
Research Articles
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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Copyright belongs to Srinakharinwirot University Engineering Journal

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