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Asymmetric Convolution-Based Neural Network for SAR Ship Detection from Scratch

Authors:

Xu WuAuthors Info & Claims

ICCPR '20: Proceedings of the 2020 9th International Conference on Computing and Pattern Recognition

Pages 90 - 95

https://doi.org/10.1145/3436369.3436464

Published: 11 January 2021 Publication History

Abstract

In recent years, an increasing number of synthetic aperture radar (SAR) researchers have applied convolutional neural network (CNN)-based optical image object detection methods to the field of SAR ship detection via transfer learning technology, and achieve well performance. However, such approaches need to load a pre-trained model for initialization, which results in the structure of CNN is fixed and it is hardly for improvement and optimization; besides, there is domain mismatch for SAR ship detection, which restricts the detection performance to some extent. In the paper, we designed two asymmetric convolution blocks that can be easily embedded in any object detection methods, namely asymmetric and square convolution feature aggregation block (A-S AB) and asymmetric and square convolution feature fusion block (A-S FB), and embedded them in the classic DSOD by replacing all of the 3 × 3 convolution layers with A-S AB and A-S FB, respectively. Experiments on the RDISD_SAR dataset demonstrate that the A-S AB contribute up to 2.86% gain of average precision (AP) while significantly reducing the number of parameter and the amount of computation; the A-S FB contribute about 1.00% AP gain at channel decay factors of 0.2, 0.3, 0.5, and 0.6, and the number of parameter and the amount of computation are also reduced to varying degrees. Compared with the original DSOD, the well performance of the two structures designed in this paper is obvious.

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Cited By

Zhang LZheng JLi CXu ZYang JWei QWu X(2024)CCDN-DETR: A Detection Transformer Based on Constrained Contrast Denoising for Multi-Class Synthetic Aperture Radar Object DetectionSensors10.3390/s2406179324:6(1793)Online publication date: 11-Mar-2024
https://doi.org/10.3390/s24061793
Li JXu CCheng PChi CYu LYu ZXu S(2023)Training SAR Ship Detectors From Scratch with Customized Convolutional Neural NetworkIEEE Transactions on Aerospace and Electronic Systems10.1109/TAES.2023.330809659:6(8624-8636)Online publication date: Dec-2023
https://doi.org/10.1109/TAES.2023.3308096
Li JChen JCheng PYu ZYu LChi C(2023)A Survey on Deep-Learning-Based Real-Time SAR Ship DetectionIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing10.1109/JSTARS.2023.324461616(3218-3247)Online publication date: 2023
https://doi.org/10.1109/JSTARS.2023.3244616
Show More Cited By

Index Terms

Asymmetric Convolution-Based Neural Network for SAR Ship Detection from Scratch
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision problems
        Object detection

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ICCPR '20: Proceedings of the 2020 9th International Conference on Computing and Pattern Recognition

October 2020

552 pages

ISBN:9781450387835

DOI:10.1145/3436369

Copyright © 2020 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Beijing University of Technology

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 11 January 2021

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Science and Technology on Complex Electronic System Simulation Laboratory

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ICCPR 2020

ICCPR 2020: 2020 9th International Conference on Computing and Pattern Recognition

October 30 - November 1, 2020

Xiamen, China

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Cited By

Zhang LZheng JLi CXu ZYang JWei QWu X(2024)CCDN-DETR: A Detection Transformer Based on Constrained Contrast Denoising for Multi-Class Synthetic Aperture Radar Object DetectionSensors10.3390/s2406179324:6(1793)Online publication date: 11-Mar-2024
https://doi.org/10.3390/s24061793
Li JXu CCheng PChi CYu LYu ZXu S(2023)Training SAR Ship Detectors From Scratch with Customized Convolutional Neural NetworkIEEE Transactions on Aerospace and Electronic Systems10.1109/TAES.2023.330809659:6(8624-8636)Online publication date: Dec-2023
https://doi.org/10.1109/TAES.2023.3308096
Li JChen JCheng PYu ZYu LChi C(2023)A Survey on Deep-Learning-Based Real-Time SAR Ship DetectionIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing10.1109/JSTARS.2023.324461616(3218-3247)Online publication date: 2023
https://doi.org/10.1109/JSTARS.2023.3244616
Cafaro MEpicoco IPulimeno MSansebastiano E(2023)Toward Enhanced Support for Ship SailingIEEE Access10.1109/ACCESS.2023.330380811(87047-87061)Online publication date: 2023
https://doi.org/10.1109/ACCESS.2023.3303808
Li JXu CSu HGao LWang T(2022)Deep Learning for SAR Ship Detection: Past, Present and FutureRemote Sensing10.3390/rs1411271214:11(2712)Online publication date: 5-Jun-2022
https://doi.org/10.3390/rs14112712

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