Automated marine oil spill detection using deep learning instance segmentation model

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...

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Main Authors: Yekeen, S.T., Balogun, A.-L.
Format: Conference or Workshop Item
Institution: Universiti Teknologi Petronas
Record Id / ISBN-0: utp-eprints.30064 /
Published: International Society for Photogrammetry and Remote Sensing 2020
Online Access: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85091137521&doi=10.5194%2fisprs-archives-XLIII-B3-2020-1271-2020&partnerID=40&md5=4c78a479f05be1034fa1109e27c175ca
http://eprints.utp.edu.my/30064/
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recordtype eprints
spelling utp-eprints.300642022-03-25T03:23:08Z Automated marine oil spill detection using deep learning instance segmentation model Yekeen, S.T. Balogun, A.-L. 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. © 2020 International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives. International Society for Photogrammetry and Remote Sensing 2020 Conference or Workshop Item NonPeerReviewed https://www.scopus.com/inward/record.uri?eid=2-s2.0-85091137521&doi=10.5194%2fisprs-archives-XLIII-B3-2020-1271-2020&partnerID=40&md5=4c78a479f05be1034fa1109e27c175ca Yekeen, S.T. and Balogun, A.-L. (2020) Automated marine oil spill detection using deep learning instance segmentation model. In: UNSPECIFIED. http://eprints.utp.edu.my/30064/
institution Universiti Teknologi Petronas
collection UTP Institutional Repository
description 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. © 2020 International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives.
format Conference or Workshop Item
author Yekeen, S.T.
Balogun, A.-L.
spellingShingle Yekeen, S.T.
Balogun, A.-L.
Automated marine oil spill detection using deep learning instance segmentation model
author_sort Yekeen, S.T.
title Automated marine oil spill detection using deep learning instance segmentation model
title_short Automated marine oil spill detection using deep learning instance segmentation model
title_full Automated marine oil spill detection using deep learning instance segmentation model
title_fullStr Automated marine oil spill detection using deep learning instance segmentation model
title_full_unstemmed Automated marine oil spill detection using deep learning instance segmentation model
title_sort automated marine oil spill detection using deep learning instance segmentation model
publisher International Society for Photogrammetry and Remote Sensing
publishDate 2020
url https://www.scopus.com/inward/record.uri?eid=2-s2.0-85091137521&doi=10.5194%2fisprs-archives-XLIII-B3-2020-1271-2020&partnerID=40&md5=4c78a479f05be1034fa1109e27c175ca
http://eprints.utp.edu.my/30064/
_version_ 1741197344031899648
score 11.62408

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