UnityShip: A Large-Scale Synthetic Dataset for Ship Recognition in Aerial Images
As a data-driven approach, deep learning requires a large amount of annotated data for training to obtain a sufficiently accurate and generalized model, especially in the field of computer vision. However, when compared with generic object recognition datasets, aerial image datasets are more challen...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-12-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/13/24/4999 |
| _version_ | 1797501028217651200 |
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| author | Boyong He Xianjiang Li Bo Huang Enhui Gu Weijie Guo Liaoni Wu |
| author_facet | Boyong He Xianjiang Li Bo Huang Enhui Gu Weijie Guo Liaoni Wu |
| author_sort | Boyong He |
| collection | DOAJ |
| description | As a data-driven approach, deep learning requires a large amount of annotated data for training to obtain a sufficiently accurate and generalized model, especially in the field of computer vision. However, when compared with generic object recognition datasets, aerial image datasets are more challenging to acquire and more expensive to label. Obtaining a large amount of high-quality aerial image data for object recognition and image understanding is an urgent problem. Existing studies show that synthetic data can effectively reduce the amount of training data required. Therefore, in this paper, we propose the first synthetic aerial image dataset for ship recognition, called UnityShip. This dataset contains over 100,000 synthetic images and 194,054 ship instances, including 79 different ship models in ten categories and six different large virtual scenes with different time periods, weather environments, and altitudes. The annotations include environmental information, instance-level horizontal bounding boxes, oriented bounding boxes, and the type and ID of each ship. This provides the basis for object detection, oriented object detection, fine-grained recognition, and scene recognition. To investigate the applications of UnityShip, the synthetic data were validated for model pre-training and data augmentation using three different object detection algorithms and six existing real-world ship detection datasets. Our experimental results show that for small-sized and medium-sized real-world datasets, the synthetic data achieve an improvement in model pre-training and data augmentation, showing the value and potential of synthetic data in aerial image recognition and understanding tasks. |
| first_indexed | 2024-03-10T03:12:22Z |
| format | Article |
| id | doaj.art-2f65da3910df4a61a6b7a4e44fb573a4 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T03:12:22Z |
| publishDate | 2021-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-2f65da3910df4a61a6b7a4e44fb573a42023-11-23T10:23:25ZengMDPI AGRemote Sensing2072-42922021-12-011324499910.3390/rs13244999UnityShip: A Large-Scale Synthetic Dataset for Ship Recognition in Aerial ImagesBoyong He0Xianjiang Li1Bo Huang2Enhui Gu3Weijie Guo4Liaoni Wu5School of Aerospace Engineering, Xiamen University, Xiamen 361102, ChinaSchool of Aerospace Engineering, Xiamen University, Xiamen 361102, ChinaSchool of Aerospace Engineering, Xiamen University, Xiamen 361102, ChinaSchool of Aeronautics and Astronautics, Shanghai Jiao Tong University, Shanghai 200240, ChinaSchool of Informatics, Xiamen University, Xiamen 361005, ChinaSchool of Aerospace Engineering, Xiamen University, Xiamen 361102, ChinaAs a data-driven approach, deep learning requires a large amount of annotated data for training to obtain a sufficiently accurate and generalized model, especially in the field of computer vision. However, when compared with generic object recognition datasets, aerial image datasets are more challenging to acquire and more expensive to label. Obtaining a large amount of high-quality aerial image data for object recognition and image understanding is an urgent problem. Existing studies show that synthetic data can effectively reduce the amount of training data required. Therefore, in this paper, we propose the first synthetic aerial image dataset for ship recognition, called UnityShip. This dataset contains over 100,000 synthetic images and 194,054 ship instances, including 79 different ship models in ten categories and six different large virtual scenes with different time periods, weather environments, and altitudes. The annotations include environmental information, instance-level horizontal bounding boxes, oriented bounding boxes, and the type and ID of each ship. This provides the basis for object detection, oriented object detection, fine-grained recognition, and scene recognition. To investigate the applications of UnityShip, the synthetic data were validated for model pre-training and data augmentation using three different object detection algorithms and six existing real-world ship detection datasets. Our experimental results show that for small-sized and medium-sized real-world datasets, the synthetic data achieve an improvement in model pre-training and data augmentation, showing the value and potential of synthetic data in aerial image recognition and understanding tasks.https://www.mdpi.com/2072-4292/13/24/4999deep learningsynthetic dataship recognitionaerial imagery |
| spellingShingle | Boyong He Xianjiang Li Bo Huang Enhui Gu Weijie Guo Liaoni Wu UnityShip: A Large-Scale Synthetic Dataset for Ship Recognition in Aerial Images Remote Sensing deep learning synthetic data ship recognition aerial imagery |
| title | UnityShip: A Large-Scale Synthetic Dataset for Ship Recognition in Aerial Images |
| title_full | UnityShip: A Large-Scale Synthetic Dataset for Ship Recognition in Aerial Images |
| title_fullStr | UnityShip: A Large-Scale Synthetic Dataset for Ship Recognition in Aerial Images |
| title_full_unstemmed | UnityShip: A Large-Scale Synthetic Dataset for Ship Recognition in Aerial Images |
| title_short | UnityShip: A Large-Scale Synthetic Dataset for Ship Recognition in Aerial Images |
| title_sort | unityship a large scale synthetic dataset for ship recognition in aerial images |
| topic | deep learning synthetic data ship recognition aerial imagery |
| url | https://www.mdpi.com/2072-4292/13/24/4999 |
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