Detection and Classification of River and Lake Signs Using the YOLO Algorithm

DOI

10.2991/978-94-6239-731-6_8How to use a DOI?

Keywords

YOLO; inland waterway signs; computer vision

Abstract

Waterway signs in rivers and lakes play a vital role in ensuring navigation safety by providing directional guidance, hazard warnings, speed limitations, and safe passage indicators for vessels. Manual inspection and monitoring systems currently used in Indonesia are time-consuming, expensive, and vulnerable to delays in detecting damaged or missing signs due to limited inspection frequency and environmental constraints. Meanwhile, the dynamic and complex characteristics of inland waterways — including small sign size in images, varying distances, fluctuating lighting conditions, and cluttered backgrounds — pose significant challenges for automated detection systems. To address these issues, this study proposes the utilization of the YOLO (You Only Look Once) algorithm for automatic detection and classification of river and lake navigation signs. YOLO is known for its speed and accuracy in real-time object detection and has demonstrated strong performance in various domains, including maritime applications. Despite its potential, research on the automated detection of inland waterway signs, particularly in Indonesia, remains limited. This study aims to develop an efficient and robust computer-vision-based system capable of identifying inland waterway signs in accordance with national regulations. The expected outcomes include improved monitoring efficiency, enhanced navigation safety through early detection of missing or damaged signs, and the creation of a localized image dataset to support future research and the advancement of artificial intelligence in aquatic transportation safety.

Copyright

© 2026 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Raden Muhamad Firzatullah
AU  - Moh. Aziz Rohman
AU  - Donny Afrizal Melayu
AU  - Arleiny
AU  - Rizal Eko
AU  - Andhika Oktariansyah
PY  - 2026
DA  - 2026/07/09
TI  - Detection and Classification of River and Lake Signs Using the YOLO Algorithm
BT  - Proceedings of the International Conference of Inland Water and Ferries Transport Polytechnic of Palembang on Technology and Environment (IWPOSPA-TE 2025)
PB  - Atlantis Press
SP  - 59
EP  - 70
SN  - 2352-5401
UR  - https://doi.org/10.2991/978-94-6239-731-6_8
DO  - 10.2991/978-94-6239-731-6_8
ID  - Firzatullah2026
ER  -