Arbitrary-angle bounding box based location for object detection in remote sensing image
Object location is a fundamental yet challenging problem in object detection. In the remote sensing image, different imaging projection directions make the same object have various rotation angles, and in some scenes, the object distribution is relatively dense. Most of the existing deep learning-ba...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2021-01-01
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| Series: | European Journal of Remote Sensing |
| Subjects: | |
| Online Access: | http://dx.doi.org/10.1080/22797254.2021.1880975 |
| _version_ | 1798033930306191360 |
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| author | Fei Sun Huanyi Li zhiyang Liu Xinyue Li Zhize Wu |
| author_facet | Fei Sun Huanyi Li zhiyang Liu Xinyue Li Zhize Wu |
| author_sort | Fei Sun |
| collection | DOAJ |
| description | Object location is a fundamental yet challenging problem in object detection. In the remote sensing image, different imaging projection directions make the same object have various rotation angles, and in some scenes, the object distribution is relatively dense. Most of the existing deep learning-based object detection algorithms utilize horizontal bounding box to locate objects, which causes inaccurate location of the objects with dense distribution or arbitrary direction, thus leading to the detection misses. In this paper, we propose an arbitrary-angle bounding box based object location and embed it into the Faster R-CNN, developing a new framework called Rotated Faster R-CNN (R-FRCNN) for object detection in remote sensing image. In R-FRCNN, we specially improve anchor ratios to adapt to the objects like ship with large aspect ratio and increase the weights of the horizontal bounding box regression to reduce the interference of the arbitrary-angle bounding box on the horizontal bounding box prediction. Comprehensive experiments on a public dataset and a self-assembled dataset (which we make publically available) show the superior performance of our method compared to standalone state-of-the-art object detectors. |
| first_indexed | 2024-04-11T20:37:13Z |
| format | Article |
| id | doaj.art-a9e06789e5d24b179715d473b2a190b6 |
| institution | Directory Open Access Journal |
| issn | 2279-7254 |
| language | English |
| last_indexed | 2024-04-11T20:37:13Z |
| publishDate | 2021-01-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | European Journal of Remote Sensing |
| spelling | doaj.art-a9e06789e5d24b179715d473b2a190b62022-12-22T04:04:20ZengTaylor & Francis GroupEuropean Journal of Remote Sensing2279-72542021-01-0154110211610.1080/22797254.2021.18809751880975Arbitrary-angle bounding box based location for object detection in remote sensing imageFei Sun0Huanyi Li1zhiyang Liu2Xinyue Li3Zhize Wu4Hefei UniversityHefei UniversityHefei UniversityUniversity of Science and Technology of ChinaHefei UniversityObject location is a fundamental yet challenging problem in object detection. In the remote sensing image, different imaging projection directions make the same object have various rotation angles, and in some scenes, the object distribution is relatively dense. Most of the existing deep learning-based object detection algorithms utilize horizontal bounding box to locate objects, which causes inaccurate location of the objects with dense distribution or arbitrary direction, thus leading to the detection misses. In this paper, we propose an arbitrary-angle bounding box based object location and embed it into the Faster R-CNN, developing a new framework called Rotated Faster R-CNN (R-FRCNN) for object detection in remote sensing image. In R-FRCNN, we specially improve anchor ratios to adapt to the objects like ship with large aspect ratio and increase the weights of the horizontal bounding box regression to reduce the interference of the arbitrary-angle bounding box on the horizontal bounding box prediction. Comprehensive experiments on a public dataset and a self-assembled dataset (which we make publically available) show the superior performance of our method compared to standalone state-of-the-art object detectors.http://dx.doi.org/10.1080/22797254.2021.1880975arbitrary anglebounding boxobject detectionremote sensing image |
| spellingShingle | Fei Sun Huanyi Li zhiyang Liu Xinyue Li Zhize Wu Arbitrary-angle bounding box based location for object detection in remote sensing image European Journal of Remote Sensing arbitrary angle bounding box object detection remote sensing image |
| title | Arbitrary-angle bounding box based location for object detection in remote sensing image |
| title_full | Arbitrary-angle bounding box based location for object detection in remote sensing image |
| title_fullStr | Arbitrary-angle bounding box based location for object detection in remote sensing image |
| title_full_unstemmed | Arbitrary-angle bounding box based location for object detection in remote sensing image |
| title_short | Arbitrary-angle bounding box based location for object detection in remote sensing image |
| title_sort | arbitrary angle bounding box based location for object detection in remote sensing image |
| topic | arbitrary angle bounding box object detection remote sensing image |
| url | http://dx.doi.org/10.1080/22797254.2021.1880975 |
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