A Benchmark Dataset for Aircraft Detection in Optical Remote Sensing Imagery
The problem is that existing aircraft detection datasets rarely simultaneously consider the diversity of target features and the complexity of environmental factors, which has become an important factor restricting the effectiveness and reliability of aircraft detection algorithms. Although a large...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-12-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/16/24/4699 |
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| _version_ | 1846102884241899520 |
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| author | Jianming Hu Xiyang Zhi Bingxian Zhang Tianjun Shi Qi Cui Xiaogang Sun |
| author_facet | Jianming Hu Xiyang Zhi Bingxian Zhang Tianjun Shi Qi Cui Xiaogang Sun |
| author_sort | Jianming Hu |
| collection | DOAJ |
| description | The problem is that existing aircraft detection datasets rarely simultaneously consider the diversity of target features and the complexity of environmental factors, which has become an important factor restricting the effectiveness and reliability of aircraft detection algorithms. Although a large amount of research has been devoted to breaking through few-sample-driven aircraft detection technology, most algorithms still struggle to effectively solve the problems of missed target detection and false alarms caused by numerous environmental interferences in bird-eye optical remote sensing scenes. To further aircraft detection research, we have established a new dataset, Aircraft Detection in Complex Optical Scene (ADCOS), sourced from various platforms including Google Earth, Microsoft Map, Worldview-3, Pleiades, Ikonos, Orbview-3, and Jilin-1 satellites. It integrates 3903 meticulously chosen images of over 400 famous airports worldwide, containing 33,831 annotated instances employing the oriented bounding box (OBB) format. Notably, this dataset encompasses a wide range of various targets characteristics including multi-scale, multi-direction, multi-type, multi-state, and dense arrangement, along with complex relationships between targets and backgrounds like cluttered backgrounds, low contrast, shadows, and occlusion interference conditions. Furthermore, we evaluated nine representative detection algorithms on the ADCOS dataset, establishing a performance benchmark for subsequent algorithm optimization. The latest dataset will soon be available on the Github website. |
| format | Article |
| id | doaj-art-c8f839e44c7e47da8fa99d9289ca27a9 |
| institution | Kabale University |
| issn | 2072-4292 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj-art-c8f839e44c7e47da8fa99d9289ca27a92024-12-27T14:50:56ZengMDPI AGRemote Sensing2072-42922024-12-011624469910.3390/rs16244699A Benchmark Dataset for Aircraft Detection in Optical Remote Sensing ImageryJianming Hu0Xiyang Zhi1Bingxian Zhang2Tianjun Shi3Qi Cui4Xiaogang Sun5Research Center for Space Optical Engineering, Harbin Institute of Technology, Harbin 150001, ChinaResearch Center for Space Optical Engineering, Harbin Institute of Technology, Harbin 150001, ChinaBeijing Institute of Space Mechanics and Electricity, Beijing 100076, ChinaResearch Center for Space Optical Engineering, Harbin Institute of Technology, Harbin 150001, ChinaResearch Center for Space Optical Engineering, Harbin Institute of Technology, Harbin 150001, ChinaSchool of Instrumentation Science and Engineering, Harbin Institute of Technology, Harbin 150001, ChinaThe problem is that existing aircraft detection datasets rarely simultaneously consider the diversity of target features and the complexity of environmental factors, which has become an important factor restricting the effectiveness and reliability of aircraft detection algorithms. Although a large amount of research has been devoted to breaking through few-sample-driven aircraft detection technology, most algorithms still struggle to effectively solve the problems of missed target detection and false alarms caused by numerous environmental interferences in bird-eye optical remote sensing scenes. To further aircraft detection research, we have established a new dataset, Aircraft Detection in Complex Optical Scene (ADCOS), sourced from various platforms including Google Earth, Microsoft Map, Worldview-3, Pleiades, Ikonos, Orbview-3, and Jilin-1 satellites. It integrates 3903 meticulously chosen images of over 400 famous airports worldwide, containing 33,831 annotated instances employing the oriented bounding box (OBB) format. Notably, this dataset encompasses a wide range of various targets characteristics including multi-scale, multi-direction, multi-type, multi-state, and dense arrangement, along with complex relationships between targets and backgrounds like cluttered backgrounds, low contrast, shadows, and occlusion interference conditions. Furthermore, we evaluated nine representative detection algorithms on the ADCOS dataset, establishing a performance benchmark for subsequent algorithm optimization. The latest dataset will soon be available on the Github website.https://www.mdpi.com/2072-4292/16/24/4699optical remote sensing imageaircraft detectioncomplex sceneenvironmental interferencesdetection benchmark |
| spellingShingle | Jianming Hu Xiyang Zhi Bingxian Zhang Tianjun Shi Qi Cui Xiaogang Sun A Benchmark Dataset for Aircraft Detection in Optical Remote Sensing Imagery Remote Sensing optical remote sensing image aircraft detection complex scene environmental interferences detection benchmark |
| title | A Benchmark Dataset for Aircraft Detection in Optical Remote Sensing Imagery |
| title_full | A Benchmark Dataset for Aircraft Detection in Optical Remote Sensing Imagery |
| title_fullStr | A Benchmark Dataset for Aircraft Detection in Optical Remote Sensing Imagery |
| title_full_unstemmed | A Benchmark Dataset for Aircraft Detection in Optical Remote Sensing Imagery |
| title_short | A Benchmark Dataset for Aircraft Detection in Optical Remote Sensing Imagery |
| title_sort | benchmark dataset for aircraft detection in optical remote sensing imagery |
| topic | optical remote sensing image aircraft detection complex scene environmental interferences detection benchmark |
| url | https://www.mdpi.com/2072-4292/16/24/4699 |
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