AccScience Publishing / AJWEP / Online First / DOI: 10.36922/ajwep.8282
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ORIGINAL RESEARCH ARTICLE

SpillNet: A modified convolutional neural network model for oil spill detection

Tokula I. Umaha1,2 ,  Felix Ale1,2 ,  Ikpaya D. Ikpaya1,2 ,  John A. Momoh1,2 ,  Steve A. Adeshina3 ,  Ilesanmi A. Daniyan4* ,  Adeyinka P. Adedigba5
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1 Department of Systems Engineering, School of Engineering, African University of Science and Technology, Garki, Abuja, Nigeria
2 National Space Research and Development Agency, Institute of Space Science and Engineering, Abuja Obasanjo Space Centre, Abuja, Nigeria
3 Department of Electrical and Electronics Engineering, Faculty of Engineering, Nile University of Nigeria, Abuja, Nigeria
4 Department of Mechatronics Engineering, College of Engineering, Bells University of Technology, Ota, Ogun, Nigeria
5 Department of Mechatronics Engineering, Faculty of Engineering and Technology, Federal University of Technology, Minna, Niger, Nigeria
AJWEP, 8282 https://doi.org/10.36922/ajwep.8282
Submitted: 29 December 2024 | Revised: 10 February 2025 | Accepted: 21 February 2025 | Published: 6 March 2025
© 2025 by the Author(s). This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution 4.0 International License ( https://creativecommons.org/licenses/by/4.0/ )
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Abstract

Rapid and accurate detection of oil spills is crucial for initiating timely response measures to mitigate environmental impacts. This study proposes an oil spill detection method based on a modified convolutional neural network, termed “SpillNet.” The architecture integrates multiple depthwise separable convolutional layers, batch normalization, and residual connections to enhance feature extraction and learning capabilities. The dataset consists of synthetic aperture radar images obtained from Sentinel-1 satellites, part of the European Space Agency’s Copernicus program. Model training was conducted on an NVIDIA Tesla T4 GPU available on Google Colab, with up to 12GB of random access memory. Programming was carried out in the Python environment using Python 3.7, and all required libraries were installed through pip. The results indicate that the proposed model achieves an accuracy of 0.946947, a mean Intersection over Union of 0.58124, and a mean specificity of 0.944469. These results demonstrate that the proposed model outperforms existing models in the oil spill segmentation task. This study contributes to advancing automated oil spill detection by offering a reliable and efficient solution for early oil spill detection and environmental monitoring.

Keywords
Batch normalization
Convolutional neural network
Model
Oil spill
Programming
Synthetic aperture radar
Funding
None.
Conflict of interest
The authors declare no conflicts of interest.
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Asian Journal of Water, Environment and Pollution, Electronic ISSN: 1875-8568 Print ISSN: 0972-9860, Published by AccScience Publishing
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