Coordinate-Aware Mask R-CNN with Group Normalization: A underwater marine animal instance segmentation framework

Dewei Yi; Hasan Bayarov Ahmedov; Shouyong Jiang; Yiren Li; Sean Joseph Flinn; Paul G. Fernandes

doi:10.1016/j.neucom.2024.127488

Coordinate-Aware Mask R-CNN with Group Normalization: A underwater marine animal instance segmentation framework

Dewei Yi, Hasan Bayarov Ahmedov, Shouyong Jiang, Yiren Li, Sean Joseph Flinn, Paul G. Fernandes

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Abstract

Unsustainable fishing, driven by bycatch and discards, harms marine ecosystems. Addressing this, we propose a Coordinate-Aware Mask R-CNN (CAM-RCNN) method to enhance fish detection in commercial trawls. Leveraging CoordConv and Group Normalization, our approach improves generalisation and stability. To tackle class imbalance, a compound Dice and cross-entropy loss is employed, and image data are enhanced through multi-scale retinex and colour restoration. Evaluating on two fishing datasets, CAM-RCNN excels in accuracy and generalisation, achieving the best Average Precision (AP) for instance mask and BBOX prediction in both source (39.7%, 40.2%) and target domains (24.4%, 24.2%). This method promotes sustainable fishing by selectively capturing desired fish, reducing harm to non-target species.

Original language	English
Article number	127488
Journal	Neurocomputing
Volume	583
Early online date	6 Mar 2024
DOIs	https://doi.org/10.1016/j.neucom.2024.127488
Publication status	Published - 28 May 2024

Keywords

Convolutional neural network (CNN)
Generalisability
Instance segmentation
Underwater dataset

ASJC Scopus subject areas

Artificial Intelligence
Cognitive Neuroscience
Computer Science Applications

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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title = "Coordinate-Aware Mask R-CNN with Group Normalization: A underwater marine animal instance segmentation framework",

abstract = "Unsustainable fishing, driven by bycatch and discards, harms marine ecosystems. Addressing this, we propose a Coordinate-Aware Mask R-CNN (CAM-RCNN) method to enhance fish detection in commercial trawls. Leveraging CoordConv and Group Normalization, our approach improves generalisation and stability. To tackle class imbalance, a compound Dice and cross-entropy loss is employed, and image data are enhanced through multi-scale retinex and colour restoration. Evaluating on two fishing datasets, CAM-RCNN excels in accuracy and generalisation, achieving the best Average Precision (AP) for instance mask and BBOX prediction in both source (39.7%, 40.2%) and target domains (24.4%, 24.2%). This method promotes sustainable fishing by selectively capturing desired fish, reducing harm to non-target species.",

keywords = "Convolutional neural network (CNN), Generalisability, Instance segmentation, Underwater dataset",

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T1 - Coordinate-Aware Mask R-CNN with Group Normalization: A underwater marine animal instance segmentation framework

AU - Yi, Dewei

AU - Ahmedov, Hasan Bayarov

AU - Jiang, Shouyong

AU - Li, Yiren

AU - Flinn, Sean Joseph

AU - Fernandes, Paul G.

PY - 2024/5/28

Y1 - 2024/5/28

N2 - Unsustainable fishing, driven by bycatch and discards, harms marine ecosystems. Addressing this, we propose a Coordinate-Aware Mask R-CNN (CAM-RCNN) method to enhance fish detection in commercial trawls. Leveraging CoordConv and Group Normalization, our approach improves generalisation and stability. To tackle class imbalance, a compound Dice and cross-entropy loss is employed, and image data are enhanced through multi-scale retinex and colour restoration. Evaluating on two fishing datasets, CAM-RCNN excels in accuracy and generalisation, achieving the best Average Precision (AP) for instance mask and BBOX prediction in both source (39.7%, 40.2%) and target domains (24.4%, 24.2%). This method promotes sustainable fishing by selectively capturing desired fish, reducing harm to non-target species.

AB - Unsustainable fishing, driven by bycatch and discards, harms marine ecosystems. Addressing this, we propose a Coordinate-Aware Mask R-CNN (CAM-RCNN) method to enhance fish detection in commercial trawls. Leveraging CoordConv and Group Normalization, our approach improves generalisation and stability. To tackle class imbalance, a compound Dice and cross-entropy loss is employed, and image data are enhanced through multi-scale retinex and colour restoration. Evaluating on two fishing datasets, CAM-RCNN excels in accuracy and generalisation, achieving the best Average Precision (AP) for instance mask and BBOX prediction in both source (39.7%, 40.2%) and target domains (24.4%, 24.2%). This method promotes sustainable fishing by selectively capturing desired fish, reducing harm to non-target species.

KW - Convolutional neural network (CNN)

KW - Generalisability

KW - Instance segmentation

KW - Underwater dataset

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VL - 583

JO - Neurocomputing

JF - Neurocomputing

M1 - 127488

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Coordinate-Aware Mask R-CNN with Group Normalization: A underwater marine animal instance segmentation framework

Abstract

Keywords

ASJC Scopus subject areas

UN SDGs

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