Version 1
: Received: 3 October 2024 / Approved: 7 October 2024 / Online: 8 October 2024 (12:00:28 CEST)
How to cite:
Łącki, M. Determining the Level of Threat in Maritime Navigation Based on the Detection of Small Floating Objects with Deep Neural Networks. Preprints2024, 2024100431. https://doi.org/10.20944/preprints202410.0431.v1
Łącki, M. Determining the Level of Threat in Maritime Navigation Based on the Detection of Small Floating Objects with Deep Neural Networks. Preprints 2024, 2024100431. https://doi.org/10.20944/preprints202410.0431.v1
Łącki, M. Determining the Level of Threat in Maritime Navigation Based on the Detection of Small Floating Objects with Deep Neural Networks. Preprints2024, 2024100431. https://doi.org/10.20944/preprints202410.0431.v1
APA Style
Łącki, M. (2024). Determining the Level of Threat in Maritime Navigation Based on the Detection of Small Floating Objects with Deep Neural Networks. Preprints. https://doi.org/10.20944/preprints202410.0431.v1
Chicago/Turabian Style
Łącki, M. 2024 "Determining the Level of Threat in Maritime Navigation Based on the Detection of Small Floating Objects with Deep Neural Networks" Preprints. https://doi.org/10.20944/preprints202410.0431.v1
Abstract
The article describes the use of deep neural networks to detect small floating objects located in the own vessel’s path. The research aimed to evaluate the performance of deep neural networks by classifying sea surface images and assigning the level of threat resulting from the detection of objects floating on the water, such as fishing nets, plastic debris or buoys. Such a solution could function as a decision support system capable of detecting and informing the watch officer or helmsman about possible threats and reducing the risk of overlooking them at a critical moment. Several neural network structures were compared to find the most efficient solution, taking into account the speed and efficiency of network training and its performance during testing. Additional time measurements have been made to test real-time capabilities of the system. The research results confirm that it is possible to create a practical lightweight detection system with Convolutional Neural Networks that calculates safety level in real time.
Keywords
deep neural networks; detection and classification; safety of marine navigation; image processing; object detection techniques
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
