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@article{204173,
        author = {VARNIKHA N and C Gomatheeswari Preethika and K Bhuvaneshwari and M Stella Mercy},
        title = {Oilspill Detection using Deep Learning Techniques},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {13},
        number = {1},
        pages = {2994-3002},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=204173},
        abstract = {Oil spills are one of the most dangerous environmental threats to marine ecosystems and coastal regions. The increasing volume of oil transported by ships amplifies the risk of spills due to accidents, leaks, or other operational failures. Rapid detection of these spills is crucial to minimizing their environmental impact and enabling timely cleanup. This research presents the automated detection of oil spills in ocean waters, utilizing deep learning techniques. Specifically, the study applies both the Inception V3 model and ResNet algorithm to analyze aerial and satellite images of the sea surface. A carefully curated dataset, containing both oil spill and non-oil spill images, is preprocessed and augmented to ensure robust model performance under various environmental conditions, such as changes in weather, lighting, and sea surface features. The primary goal of this study is to develop an efficient and accurate system for early oil spill detection, which can assist maritime agencies and environmental organizations in monitoring large oceanic areas. This system aims to improve response times, enabling faster intervention and reducing the overall environmental impact of oil spills.},
        keywords = {Oil spill Detection, Deep Learning, Inception v3, Satellite Imagery, Machine Learning, Image classification, Environmental monitoring, Marine pollution},
        month = {June},
        }