SEAN: A Simple and Efficient Attention Network for Aircraft Detection in SAR Images

Due to the unique imaging mechanism of synthetic aperture radar (SAR), which leads to a discrete state of aircraft targets in images, its detection performance is vulnerable to the influence of complex ground objects. Although existing deep learning detection algorithms show good performance, they g...

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Bibliographic Details
Published in:Remote sensing (Basel, Switzerland) Switzerland), 2022-09, Vol.14 (18), p.4669
Main Authors: Han, Ping, Liao, Dayu, Han, Binbin, Cheng, Zheng
Format: Article
Language:English
Subjects:
Accuracy
Aircraft
Aircraft detection
Airports
Algorithms
attention mechanism
Computer networks
Datasets
Deep learning
Design
False alarms
Feature extraction
Localization
Machine learning
Modules
Object recognition
Parameterization
Parameters
Radar imaging
Remote sensing
residual dilated
Semantics
structural re-parameterization
Synthetic aperture radar
synthetic aperture radar (SAR)
Target detection
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Summary:Due to the unique imaging mechanism of synthetic aperture radar (SAR), which leads to a discrete state of aircraft targets in images, its detection performance is vulnerable to the influence of complex ground objects. Although existing deep learning detection algorithms show good performance, they generally use a feature pyramid neck design and large backbone network, which reduces the detection efficiency to some extent. To address these problems, we propose a simple and efficient attention network (SEAN) in this paper, which takes YOLOv5s as the baseline. First, we shallow the depth of the backbone network and introduce a structural re-parameterization technique to increase the feature extraction capability of the backbone. Second, the neck architecture is designed by using a residual dilated module (RDM), a low-level semantic enhancement module (LSEM), and a localization attention module (LAM), substantially reducing the number of parameters and computation of the network. The results on the Gaofen-3 aircraft target dataset show that this method achieves 97.7% AP at a speed of 83.3 FPS on a Tesla M60, exceeding YOLOv5s by 1.3% AP and 8.7 FPS with 40.51% of the parameters and 86.25% of the FLOPs.
ISSN:2072-4292
2072-4292
DOI:10.3390/rs14184669