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To address the challenges of scale variation and dense object distribution in remote sensing imagery caused by varying imaging angles，a novel object detection algorithm is proposed based on multi-branch fusion self-attention（MFS）.A multi-branch module that integrates convolutional and self-attention mechanisms is designed to build a feature extraction network，and the fourth detection head is built for small objects to facilitate multi-scale feature fusion.Meanwhile，the resulting model is pruned by the DepGraph method to achieve a lightweight architecture.Experiments on the DOTA and NWPU VHR-10 datasets demonstrate that the proposed algorithm achieves mean average precision（mAP）scores of 77.7%and 96.5%respectively，outperforming the peer detectors of similar algorithm complexity.Notably，the pruned version maintains a mAP of 72.9%on DOTA，with only 6.64 million parameters.
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| 科 Family | 属数 Number of genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) | 属 Genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) |
|---|---|---|---|---|---|---|
| 鹅膏菌科Amanitaceae | 2 | 11 | 5.26 | 鹅膏菌属 Amanita | 10 | 4.78 |
| 小菇科 Mycenaceae | 2 | 12 | 5.74 | 丝盖伞属 Inocybe | 5 | 2.39 |
| 多孔菌科 Polyporaceae | 8 | 14 | 6.70 | 蜡蘑属 Laccaria | 5 | 2.39 |
| 红菇科 Russulaceae | 3 | 23 | 11.00 | 小皮伞属 Marasmius | 6 | 2.87 |
| 小菇属 Mycena | 11 | 5.26 | ||||
| 光柄菇属 Pluteus | 5 | 2.39 | ||||
| 红菇属 Russula | 17 | 8.13 | ||||
| 栓菌属 Trametes | 5 | 2.39 |
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