Remote Sensing Small Object Detection Based on Multicontextual Information Aggregation
Due to wide field of view and background confusion, remote sensing objects are small and densely packed, hence commonly used detection methods detecting small objects are not satisfactory. In this article, we propose the multicontextual information aggregation YOLO (MCIA-YOLO) method, combining thre...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
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| Online Access: | https://ieeexplore.ieee.org/document/10891715/ |
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| _version_ | 1850276198887718912 |
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| author | Jingyu Wang Mingrui Ma Pengfei Huang Shaohui Mei Liang Zhang Hongmei Wang |
| author_facet | Jingyu Wang Mingrui Ma Pengfei Huang Shaohui Mei Liang Zhang Hongmei Wang |
| author_sort | Jingyu Wang |
| collection | DOAJ |
| description | Due to wide field of view and background confusion, remote sensing objects are small and densely packed, hence commonly used detection methods detecting small objects are not satisfactory. In this article, we propose the multicontextual information aggregation YOLO (MCIA-YOLO) method, combining three novel modules to effectively aggregate multicontextual information across channels, depths, and pixels. First, the channel-spatial information aggregation module assembles spatial global features pursuant to channel contextual information, increasing the density of key information. Second, the shallow-deep information sparse aggregation module applies a sparse cross self-attention mechanism. By sparsely correlating long-range dependency information across different regions, the representation capability of a small target is enhanced while removing redundant information. Third, to enrich local multiscale features and better identify dense targets, multiscale weighted aggregation module convolves multireceptive field information and performs weighted fusion. Our method demonstrates satisfactory performance on dataset VisDrone2019, UAVDT, and NWPU VHR-10, especially in small objects detection, surpassing several state-of-the-art methods. |
| format | Article |
| id | doaj-art-e2776f65718a490d86e125c1ed5ee2ce |
| institution | OA Journals |
| issn | 1939-1404 2151-1535 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| spelling | doaj-art-e2776f65718a490d86e125c1ed5ee2ce2025-08-20T01:50:22ZengIEEEIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing1939-14042151-15352025-01-01188248826010.1109/JSTARS.2025.354318910891715Remote Sensing Small Object Detection Based on Multicontextual Information AggregationJingyu Wang0https://orcid.org/0000-0001-7017-1938Mingrui Ma1https://orcid.org/0009-0000-4078-2391Pengfei Huang2https://orcid.org/0000-0003-4092-2918Shaohui Mei3https://orcid.org/0000-0002-8018-596XLiang Zhang4Hongmei Wang5https://orcid.org/0000-0002-1108-3505School of Astronautics, Northwestern Polytechnical University, Xi'an, ChinaUnmanned System Research Institute, Northwestern Polytechnical University, Xi'an, ChinaSchool of Astronautics, Northwestern Polytechnical University, Xi'an, ChinaSchool of Electronics and Information, Northwestern Polytechnical University, Xi'an, ChinaNational Key Laboratory of Air-Based Information Perception and Fusion, Luoyang, ChinaSchool of Astronautics, Northwestern Polytechnical University, Xi'an, ChinaDue to wide field of view and background confusion, remote sensing objects are small and densely packed, hence commonly used detection methods detecting small objects are not satisfactory. In this article, we propose the multicontextual information aggregation YOLO (MCIA-YOLO) method, combining three novel modules to effectively aggregate multicontextual information across channels, depths, and pixels. First, the channel-spatial information aggregation module assembles spatial global features pursuant to channel contextual information, increasing the density of key information. Second, the shallow-deep information sparse aggregation module applies a sparse cross self-attention mechanism. By sparsely correlating long-range dependency information across different regions, the representation capability of a small target is enhanced while removing redundant information. Third, to enrich local multiscale features and better identify dense targets, multiscale weighted aggregation module convolves multireceptive field information and performs weighted fusion. Our method demonstrates satisfactory performance on dataset VisDrone2019, UAVDT, and NWPU VHR-10, especially in small objects detection, surpassing several state-of-the-art methods.https://ieeexplore.ieee.org/document/10891715/Global contextual informationmulticontextual informationmultireceptive field enhancementsmall object detectionsparse cross self-attention |
| spellingShingle | Jingyu Wang Mingrui Ma Pengfei Huang Shaohui Mei Liang Zhang Hongmei Wang Remote Sensing Small Object Detection Based on Multicontextual Information Aggregation IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Global contextual information multicontextual information multireceptive field enhancement small object detection sparse cross self-attention |
| title | Remote Sensing Small Object Detection Based on Multicontextual Information Aggregation |
| title_full | Remote Sensing Small Object Detection Based on Multicontextual Information Aggregation |
| title_fullStr | Remote Sensing Small Object Detection Based on Multicontextual Information Aggregation |
| title_full_unstemmed | Remote Sensing Small Object Detection Based on Multicontextual Information Aggregation |
| title_short | Remote Sensing Small Object Detection Based on Multicontextual Information Aggregation |
| title_sort | remote sensing small object detection based on multicontextual information aggregation |
| topic | Global contextual information multicontextual information multireceptive field enhancement small object detection sparse cross self-attention |
| url | https://ieeexplore.ieee.org/document/10891715/ |
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