Oil Spill Detection and Segmentation Using GAN‑Based Data Augmentation and Dual Attention Networks

Abstract

Up-to-date and reliable detection of oil spills is important to protect marine ecosystems and allow fast and accurate responses. In this approach, a full deep learning system for automatic oil spill detection and classification in aerial RGB images acquired from UAVs is presented. A dual- attention semantic segmentation network is selected by the system to improve the feature extraction for images taken in challenging marine ecosystems, and also a GAN-based data augmentation approach to reduce the issue of sparsely annotated data. With clear differences in appearance, using the proposed method the distinct visually identifiable oil types: rainbow, silver, brown, and black oils can all be identified and distinguished. The method also allows the generation of segmented spill maps for area and volume estimation. Experiments show that the system consistently achieves higher segmentation accuracy than traditional models. It also provides a solution to scalable real- time and practical monitoring of marine environments.