AI-Driven Deep Learning Framework for Real-Time Oil Spill Detection and Response in Marine Environments

The current methods for oil spill detection face limitations in coverage, real-time processing, and accuracy, hindering effective response to environmental hazards. These challenges include limited accessibility to remote areas, reliance on human intervention, difficulties in detecting small spills, and constraints in processing large-scale data. Addressing these issues requires advanced AI-driven solutions that enhance detection accuracy, enable real-time monitoring, and optimize resource utilization for cost-effective deployment. This research presents a novel approach to detecting oil spills using an enhanced deep learning model, integrating YOLO-V3 with MobileNet architecture. The primary objective was to improve the detection accuracy and speed of oil spill identification in satellite imagery. The model was trained and evaluated using a diverse dataset that included varying sea states, weather conditions, and oil spill sizes. The experimental results demonstrated significant improvements in precision, recall, F1-score, and mean average precision (mAP) compared to the standard YOLO-V3 model. Additionally, the model achieved superior detection speed, making it highly suitable for real-time applications. Comparative analysis with other state-of-the-art models further validated the effectiveness of the proposed approach. The scenario-based testing underscored the model's robustness in diverse operational conditions, showcasing its potential for practical deployment in oil spill monitoring systems.