A Novel Oil Spill Dataset Augmentation Framework Using Object Extraction and Image Blending Techniques

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書目詳細資料
發表在:	Remote Sensing vol. 17, no. 2 (2025), p. 336
主要作者:	Akhmedov, Farkhod
其他作者:	Khujamatov, Halimjon, Abdullaev, Mirjamol, Heung-Seok Jeon
出版:	MDPI AG
主題:	Uzbekistan Persian Gulf Gulf of Mexico Oil spills Feature extraction Environmental cleanup Deep learning Datasets Ocean models Applications programs Satellite imagery Marine ecosystems Environmental impact Machine learning Pollution detection Local economy Data augmentation Blending Remote sensing Environmental conditions Biodiversity Image quality Surveillance Object recognition Coastal ecosystems Satellites
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實物特徵
Resumen:	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.
ISSN:	2072-4292
DOI:	10.3390/rs17020336
Fuente:	Advanced Technologies & Aerospace Database