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The issues of small UAV(unmanned aerial vehicle)target size, limited pixel coverage in images, weak texture detail information, and the difficulty in effectively extracting infrared UAV target features, which lead to low detection accuracy, were addressed by proposing a multiscale learning-based target detection algorithm.A multi-scale feature fusion structure was constructed in the neck network of the model, and a multi-scale feature learning module was introduced.Features from both deep and shallow networks were cascaded to capture target features at multiple scales, enriching the semantic and feature information of the feature map, which significantly improved the detection accuracy of small UAV targets.During training, SIoU was used in place of CIoU loss, minimizing the network model′s loss and enhancing the regression accuracy. Experimental results demonstrate that, compared to other infrared small target detection algorithms and mainstream methods, the proposed approach effectively improves the detection accuracy of UAV targets and meet the detection accuracy requirements for UAV target detection in practical applications.
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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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