Attention Mask R-CNN for Ship Detection and Segmentation From Remote Sensing Images
In recent years, ship detection in satellite remote sensing images has become an important research topic. Most existing methods detect ships by using a rectangular bounding box but do not perform segmentation down to the pixel level. This paper proposes a ship detection and segmentation method base...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2020-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/8951182/ |
| _version_ | 1818994533994594304 |
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| author | Xuan Nie Mengyang Duan Haoxuan Ding Bingliang Hu Edward K. Wong |
| author_facet | Xuan Nie Mengyang Duan Haoxuan Ding Bingliang Hu Edward K. Wong |
| author_sort | Xuan Nie |
| collection | DOAJ |
| description | In recent years, ship detection in satellite remote sensing images has become an important research topic. Most existing methods detect ships by using a rectangular bounding box but do not perform segmentation down to the pixel level. This paper proposes a ship detection and segmentation method based on an improved Mask R-CNN model. Our proposed method can accurately detect and segment ships at the pixel level. By adding a bottom-up structure to the FPN structure of Mask R-CNN, the path between the lower layers and the topmost layer is shortened, allowing the lower layer features to be more effectively utilized at the top layer. In the bottom-up structure, we use channel-wise attention to assign weights in each channel and use the spatial attention mechanism to assign a corresponding weight at each pixel in the feature maps. This allows the feature maps to respond better to the target’s features. Using our method, the detection and segmentation mAPs increased from 70.6% and 62.0% to 76.1% and 65.8%, respectively. |
| first_indexed | 2024-12-20T20:59:28Z |
| format | Article |
| id | doaj.art-ea643b7985fb4a7bb90c8ca63e24c9e2 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-20T20:59:28Z |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-ea643b7985fb4a7bb90c8ca63e24c9e22022-12-21T19:26:45ZengIEEEIEEE Access2169-35362020-01-0189325933410.1109/ACCESS.2020.29645408951182Attention Mask R-CNN for Ship Detection and Segmentation From Remote Sensing ImagesXuan Nie0Mengyang Duan1https://orcid.org/0000-0003-0827-4834Haoxuan Ding2https://orcid.org/0000-0001-5444-7332Bingliang Hu3https://orcid.org/0000-0003-3216-5013Edward K. Wong4https://orcid.org/0000-0002-9292-5881School of Software, Northwestern Polytechnical University, Xi’an, ChinaSchool of Software, Northwestern Polytechnical University, Xi’an, ChinaSchool of Power and Energy, Northwestern Polytechnical University, Xi’an, ChinaXi’an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi’an, ChinaNYU Tandon School of Engineering, Brooklyn, NY, USAIn recent years, ship detection in satellite remote sensing images has become an important research topic. Most existing methods detect ships by using a rectangular bounding box but do not perform segmentation down to the pixel level. This paper proposes a ship detection and segmentation method based on an improved Mask R-CNN model. Our proposed method can accurately detect and segment ships at the pixel level. By adding a bottom-up structure to the FPN structure of Mask R-CNN, the path between the lower layers and the topmost layer is shortened, allowing the lower layer features to be more effectively utilized at the top layer. In the bottom-up structure, we use channel-wise attention to assign weights in each channel and use the spatial attention mechanism to assign a corresponding weight at each pixel in the feature maps. This allows the feature maps to respond better to the target’s features. Using our method, the detection and segmentation mAPs increased from 70.6% and 62.0% to 76.1% and 65.8%, respectively.https://ieeexplore.ieee.org/document/8951182/Computer visionobject detectionobject segmentationremote sensing |
| spellingShingle | Xuan Nie Mengyang Duan Haoxuan Ding Bingliang Hu Edward K. Wong Attention Mask R-CNN for Ship Detection and Segmentation From Remote Sensing Images IEEE Access Computer vision object detection object segmentation remote sensing |
| title | Attention Mask R-CNN for Ship Detection and Segmentation From Remote Sensing Images |
| title_full | Attention Mask R-CNN for Ship Detection and Segmentation From Remote Sensing Images |
| title_fullStr | Attention Mask R-CNN for Ship Detection and Segmentation From Remote Sensing Images |
| title_full_unstemmed | Attention Mask R-CNN for Ship Detection and Segmentation From Remote Sensing Images |
| title_short | Attention Mask R-CNN for Ship Detection and Segmentation From Remote Sensing Images |
| title_sort | attention mask r cnn for ship detection and segmentation from remote sensing images |
| topic | Computer vision object detection object segmentation remote sensing |
| url | https://ieeexplore.ieee.org/document/8951182/ |
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