Squeeze and Excitation Rank Faster R-CNN for Ship Detection in SAR Images
Synthetic aperture radar (SAR) ship detection is an important part of marine monitoring. With the development in computer vision, deep learning has been used for ship detection in SAR images such as the faster region-based convolutional neural network (R-CNN), single-shot multibox detector, and dens...
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| Vydané v: | IEEE geoscience and remote sensing letters Ročník 16; číslo 5; s. 751 - 755 |
|---|---|
| Hlavní autori: | , , , |
| Médium: | Journal Article |
| Jazyk: | English |
| Vydavateľské údaje: |
Piscataway
IEEE
01.05.2019
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
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| ISSN: | 1545-598X, 1558-0571 |
| On-line prístup: | Získať plný text |
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| Abstract | Synthetic aperture radar (SAR) ship detection is an important part of marine monitoring. With the development in computer vision, deep learning has been used for ship detection in SAR images such as the faster region-based convolutional neural network (R-CNN), single-shot multibox detector, and densely connected network. In SAR ship detection field, deep learning has much better detection performance than traditional methods on nearshore areas. This is because traditional methods need sea-land segmentation before detection, and inaccurate sea-land mask decreases its detection performance. Though current deep learning SAR ship detection methods still have many false detections in land areas, and some ships are missed in sea areas. In this letter, a new network architecture based on the faster R-CNN is proposed to further improve the detection performance by using squeeze and excitation mechanism. In order to improve performance, first, the feature maps are extracted and concatenated to obtain multiscale feature maps with ImageNet pretrained VGG network. After region of interest pooling, an encoding scale vector which has values between 0 and 1 is generated from subfeature maps. The scale vector is ranked, and only top <inline-formula> <tex-math notation="LaTeX">K </tex-math></inline-formula> values will be preserved. Other values will be set to 0. Then, the subfeature maps are recalibrated by this scale vector. The redundant subfeature maps will be suppressed by this operation, and the detection performance of detector can be improved. The experimental results based on Sentinel-1 images show that the detection performance of the proposed method achieves 0.836 which is 9.7% better than the state-of-the-art method when using F1 as matric and executes 14% faster. |
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| AbstractList | Synthetic aperture radar (SAR) ship detection is an important part of marine monitoring. With the development in computer vision, deep learning has been used for ship detection in SAR images such as the faster region-based convolutional neural network (R-CNN), single-shot multibox detector, and densely connected network. In SAR ship detection field, deep learning has much better detection performance than traditional methods on nearshore areas. This is because traditional methods need se–land segmentation before detection, and inaccurate se–land mask decreases its detection performance. Though current deep learning SAR ship detection methods still have many false detections in land areas, and some ships are missed in sea areas. In this letter, a new network architecture based on the faster R-CNN is proposed to further improve the detection performance by using squeeze and excitation mechanism. In order to improve performance, first, the feature maps are extracted and concatenated to obtain multiscale feature maps with ImageNet pretrained VGG network. After region of interest pooling, an encoding scale vector which has values between 0 and 1 is generated from subfeature maps. The scale vector is ranked, and only top [Formula Omitted] values will be preserved. Other values will be set to 0. Then, the subfeature maps are recalibrated by this scale vector. The redundant subfeature maps will be suppressed by this operation, and the detection performance of detector can be improved. The experimental results based on Sentinel-1 images show that the detection performance of the proposed method achieves 0.836 which is 9.7% better than the state-of-the-art method when using F1 as matric and executes 14% faster. Synthetic aperture radar (SAR) ship detection is an important part of marine monitoring. With the development in computer vision, deep learning has been used for ship detection in SAR images such as the faster region-based convolutional neural network (R-CNN), single-shot multibox detector, and densely connected network. In SAR ship detection field, deep learning has much better detection performance than traditional methods on nearshore areas. This is because traditional methods need sea-land segmentation before detection, and inaccurate sea-land mask decreases its detection performance. Though current deep learning SAR ship detection methods still have many false detections in land areas, and some ships are missed in sea areas. In this letter, a new network architecture based on the faster R-CNN is proposed to further improve the detection performance by using squeeze and excitation mechanism. In order to improve performance, first, the feature maps are extracted and concatenated to obtain multiscale feature maps with ImageNet pretrained VGG network. After region of interest pooling, an encoding scale vector which has values between 0 and 1 is generated from subfeature maps. The scale vector is ranked, and only top <inline-formula> <tex-math notation="LaTeX">K </tex-math></inline-formula> values will be preserved. Other values will be set to 0. Then, the subfeature maps are recalibrated by this scale vector. The redundant subfeature maps will be suppressed by this operation, and the detection performance of detector can be improved. The experimental results based on Sentinel-1 images show that the detection performance of the proposed method achieves 0.836 which is 9.7% better than the state-of-the-art method when using F1 as matric and executes 14% faster. |
| Author | Ji, Kefeng Kuang, Gangyao Lin, Zhao Leng, Xiangguang |
| Author_xml | – sequence: 1 givenname: Zhao orcidid: 0000-0002-6697-089X surname: Lin fullname: Lin, Zhao email: lzkmylz@163.com organization: State Key Laboratory of Complex Electromagnetic Environment Effects on Electronics and Information System, National University of Defense Technology, Changsha, China – sequence: 2 givenname: Kefeng surname: Ji fullname: Ji, Kefeng email: jikefeng@nudt.edu.cn organization: State Key Laboratory of Complex Electromagnetic Environment Effects on Electronics and Information System, National University of Defense Technology, Changsha, China – sequence: 3 givenname: Xiangguang orcidid: 0000-0002-9372-8118 surname: Leng fullname: Leng, Xiangguang email: luckight@163.com organization: State Key Laboratory of Complex Electromagnetic Environment Effects on Electronics and Information System, National University of Defense Technology, Changsha, China – sequence: 4 givenname: Gangyao surname: Kuang fullname: Kuang, Gangyao email: kuangyeats@hotmail.com organization: State Key Laboratory of Complex Electromagnetic Environment Effects on Electronics and Information System, National University of Defense Technology, Changsha, China |
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| SubjectTerms | Artificial neural networks Computer vision Data mining Deep learning Detection Excitation faster region-based convolutional neural network (R-CNN) Feature extraction Feature maps Image detection Image processing Image segmentation Machine learning Marine vehicles Methods Neural networks Performance enhancement Proposals Radar Radar detection Radar imaging SAR (radar) ship detection Ships Synthetic aperture radar synthetic aperture radar (SAR) Training |
| Title | Squeeze and Excitation Rank Faster R-CNN for Ship Detection in SAR Images |
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