scholarly journals Simultaneous Ship Detection and Orientation Estimation in SAR Images Based on Attention Module and Angle Regression

Sensors ◽  
2018 ◽  
Vol 18 (9) ◽  
pp. 2851 ◽  
Author(s):  
Jizhou Wang ◽  
Changhua Lu ◽  
Weiwei Jiang
Keyword(s):  
Semantic Information ◽  
Single Shot ◽  
Top Down ◽  
Aggregation Method ◽  
Sar Images ◽  
Ship Detection ◽  
Aspect Ratios ◽  
Bounding Box ◽  
Speed And Accuracy ◽  
Average Angle

Ship detection and angle estimation in SAR images play an important role in marine surveillance. Previous works have detected ships first and estimated their orientations second. This is time-consuming and tedious. In order to solve the problems above, we attempt to combine these two tasks using a convolutional neural network so that ships may be detected and their orientations estimated simultaneously. The proposed method is based on the original SSD (Single Shot Detector), but using a rotatable bounding box. This method can learn and predict the class, location, and angle information of ships using only one forward computation. The generated oriented bounding box is much tighter than the traditional bounding box and is robust to background disturbances. We develop a semantic aggregation method which fuses features in a top-down way. This method can provide abundant location and semantic information, which is helpful for classification and location. We adopt the attention module for the six prediction layers. It can adaptively select meaningful features and neglect weak ones. This is helpful for detecting small ships. Multi-orientation anchors are designed with different sizes, aspect ratios, and orientations. These can consider both speed and accuracy. Angular regression is embedded into the existing bounding box regression module, and thus the angle prediction is output with the position and score, without requiring too many extra computations. The loss function with angular regression is used for optimizing the model. AAP (average angle precision) is used for evaluating the performance. The experiments on the dataset demonstrate the effectiveness of our method.

scholarly journals Ship Detection under Complex Backgrounds Based on Accurate Rotated Anchor Boxes from Paired Semantic Segmentation

2019 ◽  
Vol 11 (21) ◽  
pp. 2506 ◽  
Author(s):  
Xiaowu Xiao ◽  
Zhiqiang Zhou ◽  
Bo Wang ◽  
Linhao Li ◽  
Lingjuan Miao
Keyword(s):  
Semantic Information ◽  
Semantic Segmentation ◽  
Detection Methods ◽  
Optical Remote Sensing ◽  
Coarse Scale ◽  
Ship Detection ◽  
Art Object ◽  
Aspect Ratios ◽  
Bounding Box ◽  
Bottom Left

It is still challenging to effectively detect ship objects in optical remote-sensing images with complex backgrounds. Many current CNN-based one-stage and two-stage detection methods usually first predefine a series of anchors with various scales, aspect ratios and angles, and then the detection results can be outputted by performing once or twice classification and bounding box regression for predefined anchors. However, most of the defined anchors have relatively low accuracy, and are useless for the following classification and regression. In addition, the preset anchors are not robust to produce good performance for other different detection datasets. To avoid the above problems, in this paper we design a paired semantic segmentation network to generate more accurate rotated anchors with smaller numbers. Specifically, the paired segmentation network predicts four parts (i.e., top-left, bottom-right, top-right, and bottom-left parts) of ships. By combining paired top-left and bottom-right parts (or top-right and bottom-left parts), we can take the minimum bounding box of these two parts as the rotated anchor. This way can be more robust to different ship datasets, and the generated anchors are more accurate and have fewer numbers. Furthermore, to effectively use fine-scale detail information and coarse-scale semantic information, we use the magnified convolutional features to classify and regress the generated rotated anchors. Meanwhile, the horizontal minimum bounding box of the rotated anchor is also used to combine more context information. We compare the proposed algorithm with state-of-the-art object-detection methods for natural images and ship-detection methods, and demonstrate the superiority of our method.

scholarly journals TWC-Net: A SAR Ship Detection Using Two-Way Convolution and Multiscale Feature Mapping

2021 ◽  
Vol 13 (13) ◽  
pp. 2558
Author(s):  
Lei Yu ◽  
Haoyu Wu ◽  
Zhi Zhong ◽  
Liying Zheng ◽  
Qiuyue Deng ◽  
...  
Keyword(s):  
Target Detection ◽  
Detection Methods ◽  
Observation System ◽  
Single Shot ◽  
Small Target ◽  
Feature Mapping ◽  
Sar Images ◽  
Two Stage ◽  
Ship Detection ◽  
Deep Feature

Synthetic aperture radar (SAR) is an active earth observation system with a certain surface penetration capability and can be employed to observations all-day and all-weather. Ship detection using SAR is of great significance to maritime safety and port management. With the wide application of in-depth learning in ordinary images and good results, an increasing number of detection algorithms began entering the field of remote sensing images. SAR image has the characteristics of small targets, high noise, and sparse targets. Two-stage detection methods, such as faster regions with convolution neural network (Faster RCNN), have good results when applied to ship target detection based on the SAR graph, but their efficiency is low and their structure requires many computing resources, so they are not suitable for real-time detection. One-stage target detection methods, such as single shot multibox detector (SSD), make up for the shortage of the two-stage algorithm in speed but lack effective use of information from different layers, so it is not as good as the two-stage algorithm in small target detection. We propose the two-way convolution network (TWC-Net) based on a two-way convolution structure and use multiscale feature mapping to process SAR images. The two-way convolution module can effectively extract the feature from SAR images, and the multiscale mapping module can effectively process shallow and deep feature information. TWC-Net can avoid the loss of small target information during the feature extraction, while guaranteeing good perception of a large target by the deep feature map. We tested the performance of our proposed method using a common SAR ship dataset SSDD. The experimental results show that our proposed method has a higher recall rate and precision, and the F-Measure is 93.32%. It has smaller parameters and memory consumption than other methods and is superior to other methods.

scholarly journals R2FA-Det: Delving into High-Quality Rotatable Boxes for Ship Detection in SAR Images

2020 ◽  
Vol 12 (12) ◽  
pp. 2031 ◽  
Author(s):  
Shiqi Chen ◽  
Jun Zhang ◽  
Ronghui Zhan
Keyword(s):  
High Speed ◽  
Detection Efficiency ◽  
Feature Representation ◽  
Sar Images ◽  
Shape Information ◽  
Ship Detection ◽  
Accurate Localization ◽  
Aspect Ratios ◽  
Non Local ◽  
Experimental Validations

Recently, convolutional neural network (CNN)-based methods have been extensively explored for ship detection in synthetic aperture radar (SAR) images due to their powerful feature representation abilities. However, there are still several obstacles hindering the development. First, ships appear in various scenarios, which makes it difficult to exclude the disruption of the cluttered background. Second, it becomes more complicated to precisely locate the targets with large aspect ratios, arbitrary orientations and dense distributions. Third, the trade-off between accurate localization and improved detection efficiency needs to be considered. To address these issues, this paper presents a rotate refined feature alignment detector (R 2 FA-Det), which ingeniously balances the quality of bounding box prediction and the high speed of the single-stage framework. Specifically, first, we devise a lightweight non-local attention module and embed it into the stem network. The recalibration of features not only strengthens the object-related features yet adequately suppresses the background interference. In addition, both forms of anchors are integrated into our modified anchor mechanism and thus can enable better representation of densely arranged targets with less computation burden. Furthermore, considering the shortcoming of the feature misalignment existing in the cascaded refinement scheme, a feature-guided alignment module which encodes both the position and shape information of current refined anchors into the feature points is adopted. Extensive experimental validations on two SAR ship datasets are performed and the results demonstrate that our algorithm has higher accuracy with faster speed than some state-of-the-art methods.

Ship detection in SAR images based on recurrent feature pyramid network and rotatable bounding box

2021 ◽  
Vol 15 (04) ◽  
Author(s):  
Jianwei Li ◽  
Congan Xu ◽  
Hang Su ◽  
Haiyang Wang ◽  
Libo Yao
Keyword(s):  
Sar Images ◽  
Ship Detection ◽  
Bounding Box ◽  
Feature Pyramid

scholarly journals Combinational Fusion and Global Attention of the Single-Shot Method for Synthetic Aperture Radar Ship Detection

2021 ◽  
Vol 13 (23) ◽  
pp. 4781
Author(s):  
Libo Xu ◽  
Chaoyi Pang ◽  
Yan Guo ◽  
Zhenyu Shu
Keyword(s):  
Remote Sensing ◽  
Synthetic Aperture Radar ◽  
Loss Function ◽  
Global Perspective ◽  
Synthetic Aperture ◽  
Single Shot ◽  
Sar Images ◽  
Ship Detection ◽  
Function Block ◽  
Aperture Radar

Synthetic Aperture Radar (SAR), an active remote sensing imaging radar technology, has certain surface penetration ability and can work all day and in all weather conditions. It is widely applied in ship detection to quickly collect ship information on the ocean surface from SAR images. However, the ship SAR images are often blurred, have large noise interference, and contain more small targets, which pose challenges to popular one-stage detectors, such as the single-shot multi-box detector (SSD). We designed a novel network structure, a combinational fusion SSD (CF-SSD), based on the framework of the original SSD, to solve these problems. It mainly includes three blocks, namely a combinational fusion (CF) block, a global attention module (GAM), and a mixed loss function block, to significantly improve the detection accuracy of SAR images and remote sensing images and maintain a fast inference speed. The CF block equips every feature map with the ability to detect objects of all sizes at different levels and forms a consistent and powerful detection structure to learn more useful information for SAR features. The GAM block produces attention weights and considers the channel attention information of various scale feature information or cross-layer maps so that it can obtain better feature representations from the global perspective. The mixed loss function block can better learn the positions of the truth anchor boxes by considering corner and center coordinates simultaneously. CF-SSD can effectively extract and fuse the features, avoid the loss of small or blurred object information, and precisely locate the object position from SAR images. We conducted experiments on the SAR ship dataset SSDD, and achieved a 90.3% mAP and fast inference speed close to that of the original SSD. We also tested our model on the remote sensing dataset NWPU VHR-10 and the common dataset VOC2007. The experimental results indicate that our proposed model simultaneously achieves excellent detection performance and high efficiency.

scholarly journals A Dual-Polarimetric SAR Ship Detection Dataset and a Memory-Augmented Autoencoder-Based Detection Method

Sensors ◽  
2021 ◽  
Vol 21 (24) ◽  
pp. 8478
Author(s):  
Yuxin Hu ◽  
Yini Li ◽  
Zongxu Pan
Keyword(s):  
High Efficiency ◽  
Detection Method ◽  
Color Image ◽  
False Alarms ◽  
Complex Data ◽  
Polarimetric Sar ◽  
Sar Images ◽  
Ship Detection ◽  
Bounding Box ◽  
Pseudo Color

With the development of imaging and space-borne satellite technology, a growing number of multipolarized SAR imageries have been implemented for object detection. However, most of the existing public SAR ship datasets are grayscale images under single polarization mode. To make full use of the polarization characteristics of multipolarized SAR, a dual-polarimetric SAR dataset specifically used for ship detection is presented in this paper (DSSDD). For construction, 50 dual-polarimetric Sentinel-1 SAR images were cropped into 1236 image slices with the size of 256 × 256 pixels. The variances and covariance of both VV and VH polarization were fused into R,G,B channels of the pseudo-color image. Each ship was labeled with both a rotatable bounding box (RBox) and a horizontal bounding box (BBox). Apart from 8-bit pseudo-color images, DSSDD also provides 16-bit complex data for readers. Two prevalent object detectors R3Det and Yolo-v4 were implemented on DSSDD to establish the baselines of the detectors with the RBox and BBox respectively. Furthermore, we proposed a weakly supervised ship detection method based on anomaly detection via advanced memory-augmented autoencoder (MemAE), which can significantly remove false alarms generated by the two-parameter CFAR algorithm applied upon our dual-polarimetric dataset. The proposed advanced MemAE method has the advantages of a lower annotation workload, high efficiency, good performance even compared with supervised methods, making it a promising direction for ship detection in dual-polarimetric SAR images. The dataset is available on github.

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