article · Frontiers in Mechanical Engineering
SpillNet, a customised convolutional neural network architecture, has been developed for oil spill detection in synthetic aperture radar imagery. To address the black-box nature of deep learning, which often hinders stakeholder trust in environmental monitoring, the model integrates five explainable artificial intelligence methods alongside new marine-specific evaluation metrics. These metrics include Marine Domain Relevance for quantifying spills, False Positive Analysis for differentiating look-alikes, and an expert-based Domain Alignment Score. Tested on representative samples from a dataset of 1,002 radar images, SpillNet attained an intersection over union segmentation accuracy of 83 percent and a validation accuracy of 90.5 percent. Among the explainability methods evaluated, Gradient-Weighted Class Activation Mapping, known as Grad-CAM, secured the highest domain alignment score. This makes the interpretable framework suitable for operational detection systems monitoring open-ocean marine environments and supporting maritime environmental protection goals.
Oil spills cause severe damage to marine life and coastal environments, but effective emergency responses require rapid and trustworthy detection. While artificial intelligence can spot spills in satellite radar data, decision-makers often distrust opaque models. Adding explainability ensures environmental monitors and emergency services understand why a model flags a potential spill, distinguishing real slicks from visual look-alikes with greater confidence.
This technology is intended for operational environmental monitoring and maritime surveillance systems to detect open-ocean oil spills. Prospective users include environmental protection agencies, maritime safety authorities, and oil and gas operators monitoring offshore infrastructure. The research demonstrates applied software validated on satellite radar image samples, indicating an applied and tested stage of research that requires further system integration before full commercial or operational deployment.
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One of the major challenges faced by marine ecosystem and the environment in general is oil spills especially in oil producing areas or areas with crude oil infrastructure. This threatens aquatic life, render the water body and the environment polluted and unsafe. However, accurate and detection could minimise the impact through a timely and effective response. Though the deployment of deep learning for oil spills detection using synthetic aperture radar (SAR) images, have proved effective, nevertheless, lack of interpretability of artificial intelligence models makes it a black-box which reduces the stakeholders’ trust especially in crucial applications such as environmental monitoring. This study demonstrates the application of explainable artificial intelligence (XAI) specifically the deep learning model for oil spill detection. The model integrates the SpillNet, a customised Convolutional Neural Network (CNN) architecture with five XAI techniques and unique evaluation metrics suitable for marine environmental monitoring were introduced. These include the Marine Domain Relevance (MDR) for the quantification of oil spill, False Positive Analysis (FPA) for look-alike discrimination and Domain Alignment Score (DAS); an expert-based checklist with composite metric. Our comprehensive evaluation of 20 representative samples from 1002 SAR images shows that Gradient-Weighted Class Activation Mapping (Grad-CAM) achieves the highest domain alignment score (0.608 ± 0.074). The proposed SpillNet model also achieved s egmentation accuracy (in terms of IoU) of 0.830 (83%) and validation accuracy of 90.5%. Thus, making it the most suitable XAI method for operational oil spill detection systems especially in open-ocean scenarios. The system directly supports several United Nations (UN) Sustainable Development Goals, including the Sustainable Development Goal (SDG) 6 (Clean Water), SDG 7 (Clean Energy), and SDG 14 (Life Underwater), by improving environmental protection through reliable AI-based monitoring systems.
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DOI: 10.3389/fmech.2026.1919085
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