Oriented Object Detection in Remote Sensing Using an Enhanced Feature Pyramid Network
Object detection in remote sensing images is a critical task within the field of remote sensing image interpretation and analysis, serving as a fundamental foundation for military surveillance and traffic guidance. Recently, although many object detection algorithms have been improved to adapt to th...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-08-01
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| Series: | Electronics |
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| Online Access: | https://www.mdpi.com/2079-9292/12/17/3559 |
| _version_ | 1797582675388661760 |
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| author | Xinyu Zhu Wei Zhou Kun Wang Bing He Ying Fu Xi Wu Jiliu Zhou |
| author_facet | Xinyu Zhu Wei Zhou Kun Wang Bing He Ying Fu Xi Wu Jiliu Zhou |
| author_sort | Xinyu Zhu |
| collection | DOAJ |
| description | Object detection in remote sensing images is a critical task within the field of remote sensing image interpretation and analysis, serving as a fundamental foundation for military surveillance and traffic guidance. Recently, although many object detection algorithms have been improved to adapt to the characteristics of remote sensing images and have achieved good performance, most of them still use horizontal bounding boxes, which struggle to accurately mark targets with multiple angles and dense arrangements in remote sensing images. We propose an oriented bounding box optical remote sensing image object detection method based on an enhanced feature pyramid, and add an attention module to suppress background noise. To begin with, we incorporate an angle prediction module that accurately locates the detection target. Subsequently, we design an enhanced feature pyramid network, utilizing deformable convolutions and feature fusion modules to enhance the feature information of rotating targets and improve the expressive capacity of features at all levels. The proposed algorithm in this paper performs well on the public DOTA dataset and HRSC2016 dataset, compared with other object detection methods, and the detection accuracy AP values of most object categories are improved by at least three percentage points. The results show that our method can accurately locate densely arranged and dynamically oriented targets, significantly reducing the risk of missing detections, and achieving higher levels of target detection accuracy. |
| first_indexed | 2024-03-10T23:25:52Z |
| format | Article |
| id | doaj.art-8c8516d9367f4b64aa38668c7ca44218 |
| institution | Directory Open Access Journal |
| issn | 2079-9292 |
| language | English |
| last_indexed | 2024-03-10T23:25:52Z |
| publishDate | 2023-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Electronics |
| spelling | doaj.art-8c8516d9367f4b64aa38668c7ca442182023-11-19T08:00:54ZengMDPI AGElectronics2079-92922023-08-011217355910.3390/electronics12173559Oriented Object Detection in Remote Sensing Using an Enhanced Feature Pyramid NetworkXinyu Zhu0Wei Zhou1Kun Wang2Bing He3Ying Fu4Xi Wu5Jiliu Zhou6College of Computer Science&Technology, Chengdu University of Information Technology, Chengdu 610225, ChinaCollege of Computer Science&Technology, Chengdu University of Information Technology, Chengdu 610225, ChinaCollege of Computer Science&Technology, Chengdu University of Information Technology, Chengdu 610225, ChinaCollege of Computer Science&Technology, Chengdu University of Information Technology, Chengdu 610225, ChinaCollege of Computer Science&Technology, Chengdu University of Information Technology, Chengdu 610225, ChinaCollege of Computer Science&Technology, Chengdu University of Information Technology, Chengdu 610225, ChinaImages and Spatial Information 2011 Collaborative Innovation Center of Sichuan Province, Chengdu 610225, ChinaObject detection in remote sensing images is a critical task within the field of remote sensing image interpretation and analysis, serving as a fundamental foundation for military surveillance and traffic guidance. Recently, although many object detection algorithms have been improved to adapt to the characteristics of remote sensing images and have achieved good performance, most of them still use horizontal bounding boxes, which struggle to accurately mark targets with multiple angles and dense arrangements in remote sensing images. We propose an oriented bounding box optical remote sensing image object detection method based on an enhanced feature pyramid, and add an attention module to suppress background noise. To begin with, we incorporate an angle prediction module that accurately locates the detection target. Subsequently, we design an enhanced feature pyramid network, utilizing deformable convolutions and feature fusion modules to enhance the feature information of rotating targets and improve the expressive capacity of features at all levels. The proposed algorithm in this paper performs well on the public DOTA dataset and HRSC2016 dataset, compared with other object detection methods, and the detection accuracy AP values of most object categories are improved by at least three percentage points. The results show that our method can accurately locate densely arranged and dynamically oriented targets, significantly reducing the risk of missing detections, and achieving higher levels of target detection accuracy.https://www.mdpi.com/2079-9292/12/17/3559remote sensing imagesobject detectionoriented bounding boxfeature fusionattention mechanism |
| spellingShingle | Xinyu Zhu Wei Zhou Kun Wang Bing He Ying Fu Xi Wu Jiliu Zhou Oriented Object Detection in Remote Sensing Using an Enhanced Feature Pyramid Network Electronics remote sensing images object detection oriented bounding box feature fusion attention mechanism |
| title | Oriented Object Detection in Remote Sensing Using an Enhanced Feature Pyramid Network |
| title_full | Oriented Object Detection in Remote Sensing Using an Enhanced Feature Pyramid Network |
| title_fullStr | Oriented Object Detection in Remote Sensing Using an Enhanced Feature Pyramid Network |
| title_full_unstemmed | Oriented Object Detection in Remote Sensing Using an Enhanced Feature Pyramid Network |
| title_short | Oriented Object Detection in Remote Sensing Using an Enhanced Feature Pyramid Network |
| title_sort | oriented object detection in remote sensing using an enhanced feature pyramid network |
| topic | remote sensing images object detection oriented bounding box feature fusion attention mechanism |
| url | https://www.mdpi.com/2079-9292/12/17/3559 |
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