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To address problems of accuracy and efficiency in automatically reading of float flowmeter at low flow rates under a complex environment in sintering kilns for new energy materials, an innovative YOLOFFM algorithm was proposed. The core improvements include reconstructing the backbone network, enhancing the neck structure, introducing an asymmetric compression decoupling head, and optimizing the loss function, which can significantly improve the efficiency and detection performance of the algorithm. The results show that this YOLOFFM algorithm has accuracy up to 99.15% and a recall rate of 98.69%, significantly reducing the number of model parameters and computational costs. Compared with various advanced algorithm models, YOLOFFM can improve accuracy while reducing the computational cost by more than 90%, fully demonstrating its high efficiency and reliability for new energy materials in a complex environment of sintering kilns.
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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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