Abstract
Abstract. This study developed a novel deep learning oil spill instance segmentation model using Mask-Region-based Convolutional Neural Network (Mask R-CNN) model which is a state-of-the-art computer vision model. A total of 2882 imageries containing oil spill, look-alike, ship, and land area after conducting different pre-processing activities were acquired. These images were subsequently sub-divided into 88% training and 12% for testing, equating to 2530 and 352 images respectively. The model training was conducted using transfer learning on a pre-trained ResNet 101 with COCO data as a backbone in combination with Feature Pyramid Network (FPN) architecture for the extraction of features at 30 epochs with 0.001 learning rate. The model’s performance was evaluated using precision, recall, and F1-measure which shows a higher performance than other existing models with value of 0.964, 0.969 and 0.968 respectively. As a specialized task, the study concluded that the developed deep learning instance segmentation model (Mask R-CNN) performs better than conventional machine learning models and semantic segmentation deep learning models in detection and segmentation of marine oil spill.
Highlights
The occurrence of oil spill has increased over time, due to the growth in global population resulting into intensive oil exploration and transportation (Yu et al, 2018, Yekeen et al, 2019, Balogun et al, 2020)
This study has developed an instance segmentation deep learning model using MaskRegion- based Convolutional Neural Network (Mask RCNN) with the aim of achieving a high precision detection, recognition and segmentation model, that can learn from the shape and texture for localization and target detection of oil spill, lookalike, ship and land area
A rapid, accurate and reliable mechanism for oil spill detection is a fundamental aspect of marine oil spill pollution decision support system
Summary
The occurrence of oil spill has increased over time, due to the growth in global population resulting into intensive oil exploration and transportation (Yu et al, 2018, Yekeen et al, 2019, Balogun et al, 2020). As to overcome the false visual appearance of look-alike as oil slick, different classification models for discrimination of oil spill and look-alike have been developed over time These models follows a three step approach of dark-spot identification, feature extraction and dark-spot classification using adaptive threshold (Vyas et al, 2015), statistics (Skøelv and Wahl, 1993), machine learning classifiers like SVM (Wan and Cheng, 2013) , decision tree and ANN (Singha et al, 2012, Singha et al, 2013). In addition to the inability to apply an end-to-end trainable framework Considering this limitation, remote sensing technologies has appear to be more promising, since it can be deployed at any time
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