Marine Oil Spill Detection with Deep Neural Networks

first_page Download PDF settings Order Article Reprints Font Type: Arial Georgia Verdana Font Size: Aa Aa Aa Line Spacing:    Column Width:    Background: Open AccessAbstract Marine Oil Spill Detection with Deep Neural Networks † by Fatih UysalFatih Uysal SciProfiles Scilit Preprints.org Google Scholar 1,*, Mesut GüvenMesut Güven SciProfiles Scilit Preprints.org Google Scholar 2 and Fırat HardalaçFırat Hardalaç SciProfiles Scilit Preprints.org Google Scholar 3 1 Department of Electrical and Electronics Engineering, Faculty of Engineering and Architecture, Kafkas University, Kars TR 36100, Turkey 2 Gendarmerie and Coast Guard Academy, Beytepe, Ankara TR 06805, Turkey 3 Department of Electrical and Electronics Engineering, Faculty of Engineering, Gazi University, Ankara TR 06570, Turkey * Author to whom correspondence should be addressed. † Presented at the 3rd International Electronic Conference on Processes—Green and Sustainable Process Engineering and Process Systems Engineering (ECP 2024), 29–31 May 2024; Available online: https://sciforum.net/event/ECP2024. Proceedings 2024, 105(1), 4; https://doi.org/10.3390/proceedings2024105004 Published: 28 May 2024 (This article belongs to the Proceedings of The 3rd International Electronic Conference on Processes—Green and Sustainable Process Engineering and Process Systems Engineering) Download keyboard_arrow_down Download PDF Download PDF with Cover Download XML Download Epub Versions Notes Keywords: artificial intelligence; deep neural networks; image classification; oil spill detection Oil spills, primarily due to accidents involving pipelines, tankers, and storage facilities, significantly impact marine life, particularly fish and shellfish. These leaks harm the fish and shellfish living in the seas. Within the scope of this study, open-source ocean images were used, with or without oil spills. In the dataset, there are 150 images in each class, and a total of 300 images. Before the training in deep neural networks for oil spill detection, data augmentation by rotating the dataset at certain angles to the right and left and various data preprocessing processes were applied. After the data augmentation process, the total amount of the dataset was increased from 300 to 400. In addition, as a dataset distribution, there are 320 images in the training data and 80 images in the validation data. Validation data were also used as test data. Deep learning-based deep neural networks MobileNetV2, Convolutional Vision Transformer (CvT), and ConvNeXT were used for classification processes in which oil spill detection with artificial intelligence is performed. When the application results were examined, it was observed that the classification results in deep neural networks had an accuracy rate of over 90%. Future work will aim to develop a real-time object detection interface that classifies and accurately locates oil spills. Author ContributionsConceptualization, F.U., M.G. and F.H.; methodology, F.U., M.G. and F.H.; software, F.U.; validation, F.U., M.G. and F.H.; writing—review and editing, F.U., M.G. and F.H. All authors have read and agreed to the published version of the manuscript.FundingThis research received no external funding.Institutional Review Board StatementNot applicable.Informed Consent StatementNot applicable.Data Availability StatementThe open source dataset used in the study was collected from Google Images in January 2023 under a Creative Commons license. This dataset are available from the corresponding author upon reasonable request.Conflicts of InterestThe authors declare no conflicts of interest.Disclaimer/Publisher's Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. © 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Share and Cite MDPI and ACS Style Uysal, F.; Güven, M.; Hardalaç, F. Marine Oil Spill Detection with Deep Neural Networks. Proceedings 2024, 105, 4. https://doi.org/10.3390/proceedings2024105004 AMA Style Uysal F, Güven M, Hardalaç F. Marine Oil Spill Detection with Deep Neural Networks. Proceedings. 2024; 105(1):4. https://doi.org/10.3390/proceedings2024105004 Chicago/Turabian Style Uysal, Fatih, Mesut Güven, and Fırat Hardalaç. 2024. "Marine Oil Spill Detection with Deep Neural Networks" Proceedings 105, no. 1: 4. https://doi.org/10.3390/proceedings2024105004 Article Metrics No No Article Access Statistics Multiple requests from the same IP address are counted as one view.