<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>Accurate and efficient detection of ships from aerial images is an intriguing and difficult task of extreme societal importance due to their implication and association with maritime infractions, and other suspicious actions. Having an automated system with the required capabilities indicates a substantial reduction in the related man-hours of characterization and the overall underlying processes. With the advent of various image processing techniques and advancements in the field of machine learning and deep learning, specialized methodologies can be created for the said task. An intuition for the enhancement of existing methodologies would be a study on attention-based cognition and the development of improved neural architectures with the available attention modules. This paper offers a novel study and empirical analysis of the utility of various attention modules with U-Net and other subsidiary architectures as a backbone for the task of computationally efficient and accurate ship detection. The best performing models are depicted and explained thoroughly, while considering their temporal performance.</p>
</abstract>

Accurate and efficient detection of ships from aerial images is an intriguing and difficult task of extreme societal importance due to their implication and association with maritime infractions, and other suspicious actions. Having an automated system with the required capabilities indicates a substantial reduction in the related man-hours of characterization and the overall underlying processes. With the advent of various image processing techniques and advancements in the field of machine learning and deep learning, specialized methodologies can be created for the said task. An intuition for the enhancement of existing methodologies would be a study on attention-based cognition and the development of improved neural architectures with the available attention modules. This paper offers a novel study and empirical analysis of the utility of various attention modules with U-Net and other subsidiary architectures as a backbone for the task of computationally efficient and accurate ship detection. The best performing models are depicted and explained thoroughly, while considering their temporal performance.

Applicability analysis of attention U-Nets over vanilla variants for automated ship detection

Department of Computer Science and Engineering

Institute of Technology, Nirma Univeristy

Reports on Geodesy and Geoinformatics

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Accurate and efficient detection of ships from aerial images is an intriguing and difficult task of extreme societal importance due to their implication and...

Applicability analysis of attention U-Nets over vanilla variants for automated ship detection

Data publikacji: 28 paź 2022

Zakres stron: 9 - 14

Otrzymano: 22 maj 2022

Przyjęty: 28 wrz 2022

DOI: https://doi.org/10.2478/rgg-2022-0005

Słowa kluczowe
deep learning, attention, ship detection, U-Net

© 2022 Pranshav Gajjar et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Applicability analysis of attention U-Nets over vanilla variants for automated ship detection

Data publikacji: 28 paź 2022

Zakres stron: 9 - 14

Otrzymano: 22 maj 2022

Przyjęty: 28 wrz 2022

DOI: https://doi.org/10.2478/rgg-2022-0005

Słowa kluczowedeep learning, attention, ship detection, U-Net

© 2022 Pranshav Gajjar et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Słowa kluczowe
deep learning, attention, ship detection, U-Net