IMPROVING THE ACCURACY OF AN OIL SPILL DETECTION AND CLASSIFICATION MODEL WITH FAKE DATASETS

Abstract. Deep learning is a popular tool for object detection, including oil spill detection. However, acquiring sufficient data for training deep learning models can be challenging, particularly for offshore oil spill accidents. Data augmentation is an effective solution to this issue. This study proposes a data augmentation method using a conditional-GAN model, specifically Pix2Pix, to generate dummy datasets of oil spills. These datasets were used to train the DaNet model for oil detection and classification. Results show that using the dummy datasets improves the mIoU and f1-score to 2.56% and 1.69%, respectively, and enhances the accuracy of classifying of each oil in the model. This approach not only improves the accuracy of the deep learning model but also presents a direction for data enhancement in detection or segmentation tasks for formless objects, such as oil spills, cracks, water seepage, and mildew.