A Deep Learning Framework for Real-time Oil Spill Detection and Classification (regular paper)

Efficient detection of oil spills is critical for minimizing environmental damage. This study introduces a novel approach utilizing deep learning, specifically the YOLOv8 architecture, augmented with advanced computer vision techniques for oil spill detection. Through meticulous dataset curation and model training, the YOLOv8 model achieved an impressive overall accuracy (R-score) of 0.531 and a Mean Average Precision (mAP) of 0.549. Performance varied across different spill types, with the model demonstrating notable accuracy in distinguishing between oil spills and natural features, achieving precision and recall rates of up to 0.75 and 0.68, respectively, for sheen detection. Visualizations such as box loss, class loss, and confusion matrices provide insights into the model’s performance dynamics, revealing a steady decrease in losses and an improvement in accuracy over epochs. In this dataset, the measurements are drone measurements performed by Port of Antwerp Bruges. Furthermore, practical applications showcase the model’s versatility in detecting various oil spill types in both image and video data, affirming its potential for real-world deployment in environmental monitoring and disaster response scenarios. This research represents a significant stride towards more effective oil spill detection, contributing to environmental sustainability and resilience efforts.