Attentional Feature Refinement and Alignment Network for Aircraft
Detection in SAR Imagery
- URL: http://arxiv.org/abs/2201.07124v2
- Date: Wed, 19 Jan 2022 04:37:26 GMT
- Title: Attentional Feature Refinement and Alignment Network for Aircraft
Detection in SAR Imagery
- Authors: Yan Zhao, Lingjun Zhao, Zhong Liu, Dewen Hu, Gangyao Kuang, Li Liu
- Abstract summary: Aircraft detection in Synthetic Aperture Radar (SAR) imagery is a challenging task due to aircraft's discrete appearance, obvious intraclass variation, small size and serious background's interference.
In this paper, a single-shot detector namely Attentional Feature Refinement and Alignment Network (AFRAN) is proposed for detecting aircraft in SAR images with competitive accuracy and speed.
- Score: 24.004052923372548
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Aircraft detection in Synthetic Aperture Radar (SAR) imagery is a challenging
task in SAR Automatic Target Recognition (SAR ATR) areas due to aircraft's
extremely discrete appearance, obvious intraclass variation, small size and
serious background's interference. In this paper, a single-shot detector namely
Attentional Feature Refinement and Alignment Network (AFRAN) is proposed for
detecting aircraft in SAR images with competitive accuracy and speed.
Specifically, three significant components including Attention Feature Fusion
Module (AFFM), Deformable Lateral Connection Module (DLCM) and Anchor-guided
Detection Module (ADM), are carefully designed in our method for refining and
aligning informative characteristics of aircraft. To represent characteristics
of aircraft with less interference, low-level textural and high-level semantic
features of aircraft are fused and refined in AFFM throughly. The alignment
between aircraft's discrete back-scatting points and convolutional sampling
spots is promoted in DLCM. Eventually, the locations of aircraft are predicted
precisely in ADM based on aligned features revised by refined anchors. To
evaluate the performance of our method, a self-built SAR aircraft sliced
dataset and a large scene SAR image are collected. Extensive quantitative and
qualitative experiments with detailed analysis illustrate the effectiveness of
the three proposed components. Furthermore, the topmost detection accuracy and
competitive speed are achieved by our method compared with other
domain-specific,e.g., DAPN, PADN, and general CNN-based methods,e.g., FPN,
Cascade R-CNN, SSD, RefineDet and RPDet.
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