An Intelligent Drone-Assisted Framework for Re-al-Time Oil Spill Detection Using SAR Image Analysis and Computer Vision
Authors-T.Pravalika, M Karishma
Keyword-Oil Spill Detection, Drone Surveillance, Synthetic Aperture Radar (SAR), Computer Vision, OpenCV, Image Processing, Real-Time Monitoring, Contour Detection, Marine Pollution, Environmental Monitoring.
Oil spills pose a significant threat to marine biodiversity, coastal ecosystems, and maritime in-dustries, making rapid detection and monitoring essential for minimizing environmental damage. This research introduces an intelligent oil spill detection framework that integrates drone-based surveillance with Synthetic Aperture Radar (SAR) image analysis for continuous environmental monitoring. The proposed system processes live aerial video captured by drones together with uploaded SAR images using grayscale image conversion, threshold segmentation, contour ex-traction, and image processing techniques implemented through OpenCV. A graphical user interface allows users to switch between original and processed views for easier validation of detected oil spill regions. The framework estimates spill location and affected area while provid-ing real-time visualization to support rapid emergency response. Experimental evaluation demonstrates reliable detection performance under varying environmental conditions while main-taining low processing latency suitable for real-time applications. The combination of drone imaging and SAR-based analysis provides an efficient, scalable, and cost-effective solution for marine oil spill monitoring and environmental protection.
Doi-[https://doi.org/10.5281/zenodo.21678402]