Deep Learning Approaches for Marine Oil Spill Detection and Monitoring

Abstract

Oil spill disasters severely impact marine ecosystems and coastal economies, causing lasting environmental and financial harm. Early detection is critical to mitigate damage, but traditional methods relying on Convolutional Neural Networks (CNNs) face limitations in accuracy and scalability when analyzing large datasets for real-time monitoring. These challenges highlight the need for advanced solutions to improve efficiency and reliability in detecting oil spills. To address these issues, the YOLO v12 model has been proposed for oil spill detection. YOLO (You Only Look Once) is a state-of-the-art real-time object detection algorithm that surpasses traditional CNN-based methods in speed and precision. YOLO v12 builds on previous versions, offering enhanced detection accuracy and the ability to process extensive datasets efficiently. Its streamlined architecture allows for single-pass image analysis, ensuring rapid identification of oil spills. This makes it particularly suited for time-sensitive applications where swift responses are essential to minimize environmental and economic consequences. Integrating YOLO v12 into detection systems enables continuous monitoring through satellite or drone imagery, significantly improving detection speed and scalability for vast water bodies. This innovation enhances environmental protection and strengthens disaster response operations, making it a transformative tool in combating the effects of oil spill disasters.