Lightweight Real-Time Oil Spill Segmentation With YoloV8: A Benchmark Study For UAE Coastal Monitoring

Author(s)	Ms. Saiyette Aima
Country	United Arab Emirates
Abstract	Oil spills remain a major threat to the environment and economy, especially in regions like the Arabian Gulf. In the UAE, where offshore oil production and heavy maritime traffic are concentrated, rapid and reliable detection is essential. This study compares U-Net and YOLOv8 segmentation models on a public dataset and finds YOLOv8 more effective, particularly in identifying minority oil regions where U-Net struggled. With threshold tuning to manage false alerts, the results show that lightweight deep learning models can support real-time oil spill monitoring, in line with the UAE’s Net Zero 2050 goals.
Keywords	Oil Spill Segmentation, YOLOv8, UAE Coastal Monitoring, Deep Learning, Environmental AI
Field	Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In	Volume 7, Issue 6, November-December 2025
Published On	2025-11-25
DOI	https://doi.org/10.36948/ijfmr.2025.v07i06.61639
Short DOI	https://doi.org/hbcnvj

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International Journal For Multidisciplinary Research