Context-Preserving Instance-Level Augmentation and Deformable
Convolution Networks for SAR Ship Detection
- URL: http://arxiv.org/abs/2202.06513v1
- Date: Mon, 14 Feb 2022 07:01:01 GMT
- Title: Context-Preserving Instance-Level Augmentation and Deformable
Convolution Networks for SAR Ship Detection
- Authors: Taeyong Song, Sunok Kim, SungTai Kim, Jaeseok Lee and Kwanghoon Sohn
- Abstract summary: Shape deformation of targets in SAR image due to random orientation and partial information loss is an essential challenge in SAR ship detection.
We propose a data augmentation method to train a deep network that is robust to partial information loss within the targets.
- Score: 50.53262868498824
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: Shape deformation of targets in SAR image due to random orientation and
partial information loss caused by occlusion of the radar signal, is an
essential challenge in SAR ship detection. In this paper, we propose a data
augmentation method to train a deep network that is robust to partial
information loss within the targets. Taking advantage of ground-truth
annotations for bounding box and instance segmentation mask, we present a
simple and effective pipeline to simulate information loss on targets in
instance-level, while preserving contextual information. Furthermore, we adopt
deformable convolutional network to adaptively extract shape-invariant deep
features from geometrically translated targets. By learning sampling offset to
the grid of standard convolution, the network can robustly extract the features
from targets with shape variations for SAR ship detection. Experiments on the
HRSID dataset including comparisons with other deep networks and augmentation
methods, as well as ablation study, demonstrate the effectiveness of our
proposed method.
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