The visual similarity of oil slick and other elements, known as look-alike, affects the reliability of synthetic aperture radar (SAR) images for marine oil spill detection. So far, detection and discrimination of oil spill and look-alike are still limited to the use of traditional machine learning algorithms and semantic segmentation deep learning models with limited accuracy. Thus, this study developed a novel deep learning oil spill detection model using computer vision instance segmentation Mask-Region-based Convolutional Neural Network (Mask R-CNN) model. The model training was conducted using transfer learning on the ResNet 101 on COCO as backbone in combination with Feature Pyramid Network (FPN) architecture for feature extraction at 30 epochs with 0.001 learning rate. Testing of the model was conducted using the least training and validation loss value on the withheld testing images. The model’s performance was evaluated using precision, recall, specificity, IoU, F1-measure and overall accuracy values. Ship detection and segmentation had the highest performance with overall accuracy of 98.3%. The model equally showed a higher accuracy for oil spill and look-alike detection and segmentation although oil spill detection outperformed look-alike with overall accuracy values of 96.6% and 91.0% respectively. The study concluded that the deep learning instance segmentation model performs better than conventional machine learning models and deep learning semantic segmentation models in detection and segmentation.

浮油和其他元素的外观相似性（称为相似性）会影响用于海上溢油检测的合成孔径雷达（SAR）图像的可靠性。到目前为止，漏油和相似性的检测和区分仍然仅限于使用传统的机器学习算法和语义分割深度学习模型，而这些模型的准确性有限。因此，本研究使用计算机视觉实例分割基于遮罩区域的卷积神经网络（Mask R-CNN）模型开发了一种新型的深度学习漏油检测模型。使用在COCO上的ResNet 101上作为主干的转移学习，结合特征金字塔网络（FPN）架构，以30个时间段以0.001的学习速率进行特征提取，进行了模型训练。使用最小的训练和验证损失值对保留的测试图像进​​行模型测试。使用精度，召回率，特异性，IoU，F1度量和总体精度值评估模型的性能。船舶检测和分割的性能最高，总体准确度为98.3％。该模型同样显示出较高的漏油，相似检测和细分精度，尽管漏油检测的总体准确性分别超过了相似的96.6％和91.0％。研究得出的结论是，深度学习实例分割模型在检测和分割方面比传统的机器学习模型和深度学习语义分割模型表现更好。特异性，IoU，F1量度和总体准确性值。船舶检测和分割的性能最高，总体准确度为98.3％。该模型同样显示出更高的漏油，相似检测和细分精度，尽管漏油检测的总体准确性分别超过了相似的96.6％和91.0％。研究得出的结论是，深度学习实例分割模型在检测和分割方面比传统的机器学习模型和深度学习语义分割模型表现更好。特异性，IoU，F1量度和总体准确性值。船舶检测和分割的性能最高，总体准确度为98.3％。该模型同样显示出较高的漏油，相似检测和细分精度，尽管漏油检测的总体准确性分别超过了相似的96.6％和91.0％。研究得出的结论是，深度学习实例分割模型在检测和分割方面比传统的机器学习模型和深度学习语义分割模型表现更好。该模型同样显示出较高的漏油，相似检测和细分精度，尽管漏油检测的总体准确性分别超过了相似的96.6％和91.0％。研究得出的结论是，深度学习实例分割模型在检测和分割方面比传统的机器学习模型和深度学习语义分割模型表现更好。该模型同样显示出较高的漏油，相似检测和细分精度，尽管漏油检测的总体准确性分别超过了相似的96.6％和91.0％。研究得出的结论是，深度学习实例分割模型在检测和分割方面比传统的机器学习模型和深度学习语义分割模型表现更好。