Ship Detection Based on Improved R-FCN
Aiming at the problem of detecting different sizes and types of ships in complex sea conditions, a ship detection method based on deep learning is proposed, which is mainly for the improvement of regional fully convolutional networks (R-FCN). Firstly, the ResNet50 network is selected for automatic e...
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| Format: | Article |
| Language: | zho |
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Journal of Computer Engineering and Applications Beijing Co., Ltd., Science Press
2020-06-01
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| Series: | Jisuanji kexue yu tansuo |
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| Online Access: | http://fcst.ceaj.org/CN/abstract/abstract2238.shtml |
| _version_ | 1818443618111717376 |
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| author | HUANG Zhijun, SANG Qingbing |
| author_facet | HUANG Zhijun, SANG Qingbing |
| author_sort | HUANG Zhijun, SANG Qingbing |
| collection | DOAJ |
| description | Aiming at the problem of detecting different sizes and types of ships in complex sea conditions, a ship detection method based on deep learning is proposed, which is mainly for the improvement of regional fully convolutional networks (R-FCN). Firstly, the ResNet50 network is selected for automatic extraction of features, and the feature map is automatically provided for the improved R-FCN. Secondly, the R-FCN is improved according to the characteristics of the ship identification, which allows the R-FCN to fully perform its performance on ship detection. Finally, according to the problem that the recognition rate of small ships in some categories is small, on the first step, the method of Maxpooling increases the recognition rate of small ships by 4.08 percentage points; on the second step, the improvement of ROIAlign makes the improved R-FCN in this paper perform much better on small target ship identification than original R-FCN, and the recognition rate is increased by 13 percentage points totally. This paper is also compared with the current mainstream target detection algorithms such as Faster-RCNN. Experimental results show that the method has higher recognition accuracy and the rate is basically the same as other methods. |
| first_indexed | 2024-12-14T19:02:54Z |
| format | Article |
| id | doaj.art-904969a51bb749d8814105890bfbdd45 |
| institution | Directory Open Access Journal |
| issn | 1673-9418 |
| language | zho |
| last_indexed | 2024-12-14T19:02:54Z |
| publishDate | 2020-06-01 |
| publisher | Journal of Computer Engineering and Applications Beijing Co., Ltd., Science Press |
| record_format | Article |
| series | Jisuanji kexue yu tansuo |
| spelling | doaj.art-904969a51bb749d8814105890bfbdd452022-12-21T22:50:55ZzhoJournal of Computer Engineering and Applications Beijing Co., Ltd., Science PressJisuanji kexue yu tansuo1673-94182020-06-011461045105310.3778/j.issn.1673-9418.1904061Ship Detection Based on Improved R-FCNHUANG Zhijun, SANG Qingbing0School of Internet of Things Engineering, Jiangnan University, Wuxi, Jiangsu 214122, ChinaAiming at the problem of detecting different sizes and types of ships in complex sea conditions, a ship detection method based on deep learning is proposed, which is mainly for the improvement of regional fully convolutional networks (R-FCN). Firstly, the ResNet50 network is selected for automatic extraction of features, and the feature map is automatically provided for the improved R-FCN. Secondly, the R-FCN is improved according to the characteristics of the ship identification, which allows the R-FCN to fully perform its performance on ship detection. Finally, according to the problem that the recognition rate of small ships in some categories is small, on the first step, the method of Maxpooling increases the recognition rate of small ships by 4.08 percentage points; on the second step, the improvement of ROIAlign makes the improved R-FCN in this paper perform much better on small target ship identification than original R-FCN, and the recognition rate is increased by 13 percentage points totally. This paper is also compared with the current mainstream target detection algorithms such as Faster-RCNN. Experimental results show that the method has higher recognition accuracy and the rate is basically the same as other methods.http://fcst.ceaj.org/CN/abstract/abstract2238.shtmldeep learningtarget detectionregional fully convolutional networks (r-fcn) |
| spellingShingle | HUANG Zhijun, SANG Qingbing Ship Detection Based on Improved R-FCN Jisuanji kexue yu tansuo deep learning target detection regional fully convolutional networks (r-fcn) |
| title | Ship Detection Based on Improved R-FCN |
| title_full | Ship Detection Based on Improved R-FCN |
| title_fullStr | Ship Detection Based on Improved R-FCN |
| title_full_unstemmed | Ship Detection Based on Improved R-FCN |
| title_short | Ship Detection Based on Improved R-FCN |
| title_sort | ship detection based on improved r fcn |
| topic | deep learning target detection regional fully convolutional networks (r-fcn) |
| url | http://fcst.ceaj.org/CN/abstract/abstract2238.shtml |
| work_keys_str_mv | AT huangzhijunsangqingbing shipdetectionbasedonimprovedrfcn |