A Novel Oil Spill Dataset Augmentation Framework Using Object Extraction and Image Blending Techniques
Oil spills pose significant threats to marine and coastal ecosystems, biodiversity and local economies, necessitating efficient and accurate detection systems. Traditional detection methods, such as manual inspection and satellite imaging, are often resource-intensive and time consuming. This study...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-01-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/17/2/336 |
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| author | Farkhod Akhmedov Halimjon Khujamatov Mirjamol Abdullaev Heung-Seok Jeon |
| author_facet | Farkhod Akhmedov Halimjon Khujamatov Mirjamol Abdullaev Heung-Seok Jeon |
| author_sort | Farkhod Akhmedov |
| collection | DOAJ |
| description | Oil spills pose significant threats to marine and coastal ecosystems, biodiversity and local economies, necessitating efficient and accurate detection systems. Traditional detection methods, such as manual inspection and satellite imaging, are often resource-intensive and time consuming. This study addresses these challenges by developing a novel approach to enhance the quality and diversity of oil spill datasets. Several studies have mentioned that the quality and size of a dataset is crucial for training robust vision-based deep learning models. The proposed methodology combines advanced object extraction techniques with traditional data augmentation strategies to generate high quality and realistic oil spill images under various oceanic conditions. A key innovation in this work is the application of image blending techniques, which ensure seamless integration of target oil spill features into diverse environmental ocean contexts. To facilitate accessibility and usability, a Gradio-based web application was developed, featuring a user-friendly interface that allows users to input target and source images, customize augmentation parameters, and execute the augmentation process effectively. By enriching oil spill datasets with realistic and varied scenarios, this research aimed to improve the generalizability and accuracy of deep learning models for oil spill detection. For this, we proposed three key approaches, including oil spill dataset creation from an internet source, labeled oil spill regions extracted for blending with a background image, and the creation of a Gradio web application for simplifying the oil spill dataset generation process. |
| format | Article |
| id | doaj-art-9eb549b6d905428aa9f21627607df0f0 |
| institution | Kabale University |
| issn | 2072-4292 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj-art-9eb549b6d905428aa9f21627607df0f02025-01-24T13:48:10ZengMDPI AGRemote Sensing2072-42922025-01-0117233610.3390/rs17020336A Novel Oil Spill Dataset Augmentation Framework Using Object Extraction and Image Blending TechniquesFarkhod Akhmedov0Halimjon Khujamatov1Mirjamol Abdullaev2Heung-Seok Jeon3Department of Computer Engineering, Gachon University Sujeong-Gu, Seongnam-si 461-701, Gyeonggi-do, Republic of KoreaDepartment of Computer Engineering, Gachon University Sujeong-Gu, Seongnam-si 461-701, Gyeonggi-do, Republic of KoreaDepartment of Information Systems and Technologies, Tashkent State University of Economics, Tashkent 100066, UzbekistanDepartment of Computer Engineering, Konkuk University, 268 Chungwon-daero, Chungju-si 27478, Chungcheongbuk-do, Republic of KoreaOil spills pose significant threats to marine and coastal ecosystems, biodiversity and local economies, necessitating efficient and accurate detection systems. Traditional detection methods, such as manual inspection and satellite imaging, are often resource-intensive and time consuming. This study addresses these challenges by developing a novel approach to enhance the quality and diversity of oil spill datasets. Several studies have mentioned that the quality and size of a dataset is crucial for training robust vision-based deep learning models. The proposed methodology combines advanced object extraction techniques with traditional data augmentation strategies to generate high quality and realistic oil spill images under various oceanic conditions. A key innovation in this work is the application of image blending techniques, which ensure seamless integration of target oil spill features into diverse environmental ocean contexts. To facilitate accessibility and usability, a Gradio-based web application was developed, featuring a user-friendly interface that allows users to input target and source images, customize augmentation parameters, and execute the augmentation process effectively. By enriching oil spill datasets with realistic and varied scenarios, this research aimed to improve the generalizability and accuracy of deep learning models for oil spill detection. For this, we proposed three key approaches, including oil spill dataset creation from an internet source, labeled oil spill regions extracted for blending with a background image, and the creation of a Gradio web application for simplifying the oil spill dataset generation process.https://www.mdpi.com/2072-4292/17/2/336data augmentationoil spillsimage processingimage blending |
| spellingShingle | Farkhod Akhmedov Halimjon Khujamatov Mirjamol Abdullaev Heung-Seok Jeon A Novel Oil Spill Dataset Augmentation Framework Using Object Extraction and Image Blending Techniques Remote Sensing data augmentation oil spills image processing image blending |
| title | A Novel Oil Spill Dataset Augmentation Framework Using Object Extraction and Image Blending Techniques |
| title_full | A Novel Oil Spill Dataset Augmentation Framework Using Object Extraction and Image Blending Techniques |
| title_fullStr | A Novel Oil Spill Dataset Augmentation Framework Using Object Extraction and Image Blending Techniques |
| title_full_unstemmed | A Novel Oil Spill Dataset Augmentation Framework Using Object Extraction and Image Blending Techniques |
| title_short | A Novel Oil Spill Dataset Augmentation Framework Using Object Extraction and Image Blending Techniques |
| title_sort | novel oil spill dataset augmentation framework using object extraction and image blending techniques |
| topic | data augmentation oil spills image processing image blending |
| url | https://www.mdpi.com/2072-4292/17/2/336 |
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