Combining a single shot multibox detector with transfer learning for ship detection using sentinel-1 SAR images

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.

Download Full-text

Combing Single Shot Multibox Detector with transfer learning for ship detection using Chinese Gaofen-3 images

2017 Progress in Electromagnetics Research Symposium - Fall (PIERS - FALL) ◽

10.1109/piers-fall.2017.8293227 ◽

2017 ◽

Cited By ~ 6

Author(s):

Yuanyuan Wang ◽

Chao Wang ◽

Hong Zhang ◽

Cheng Zhang ◽

Qiaoyan Fu

Keyword(s):

Transfer Learning ◽

Single Shot ◽

Ship Detection

Download Full-text

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

Sensors ◽

10.3390/s18092851 ◽

2018 ◽

Vol 18 (9) ◽

pp. 2851 ◽

Cited By ~ 20

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.

Download Full-text

Combining single shot multibox detector with transfer learning for ship detection using Sentinel-1 images

2017 SAR in Big Data Era: Models, Methods and Applications (BIGSARDATA) ◽

10.1109/bigsardata.2017.8124924 ◽

2017 ◽

Cited By ~ 8

Author(s):

Yuanyuan Wang ◽

Chao Wang ◽

Hong Zhang

Keyword(s):

Transfer Learning ◽

Single Shot ◽

Ship Detection

Download Full-text

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

Remote Sensing ◽

10.3390/rs13234781 ◽

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.

Download Full-text