AUTOMATED MARINE OIL SPILL DETECTION USING DEEP LEARNING INSTANCE SEGMENTATION MODEL Article Swipe

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Shamsudeen Temitope Yekeen , Abdul‐Lateef Balogun ·

YOU? · · 2020 · Open Access · · DOI: https://doi.org/10.5194/isprs-archives-xliii-b3-2020-1271-2020

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.

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Artificial Intelligence

Segmentation Fault

Deep Learning

Computer Science

Convolutional Neural Network

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Artificial intelligence Segmentation Deep learning Computer science Convolutional neural network Pyramid (geometry) Transfer of learning Pattern recognition (psychology) Feature (linguistics) Task (project management) Object detection F1 score Engineering Mathematics Geometry Linguistics Philosophy Systems engineering

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Type: article
Language: en
Landing Page: https://doi.org/10.5194/isprs-archives-xliii-b3-2020-1271-2020
PDF: https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLIII-B3-2020/1271/2020/isprs-archives-XLIII-B3-2020-1271-2020.pdf
OA Status: diamond
Cited By: 23
References: 29
Related Works: 10
OpenAlex ID: https://openalex.org/W3080791784

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DOI: https://doi.org/10.5194/isprs-archives-xliii-b3-2020-1271-2020

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Title: AUTOMATED MARINE OIL SPILL DETECTION USING DEEP LEARNING INSTANCE SEGMENTATION MODEL

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Language: en

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Publication year: 2020

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Publication date: 2020-08-21

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Authors: Shamsudeen Temitope Yekeen, Abdul‐Lateef Balogun

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Landing page: https://doi.org/10.5194/isprs-archives-xliii-b3-2020-1271-2020

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PDF URL: https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLIII-B3-2020/1271/2020/isprs-archives-XLIII-B3-2020-1271-2020.pdf

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Concepts: Artificial intelligence, Segmentation, Deep learning, Computer science, Convolutional neural network, Pyramid (geometry), Transfer of learning, Pattern recognition (psychology), Feature (linguistics), Task (project management), Object detection, F1 score, Engineering, Mathematics, Geometry, Linguistics, Philosophy, Systems engineering

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Cited by: 23

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Citations by year (recent): 2025: 6, 2024: 3, 2023: 3, 2022: 6, 2021: 4

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