Full
Outline
Aiming at the difficulty of foam size extraction in flotation process, a flotation foam size extraction method based on improved YOLOv11n-seg was proposed. Firstly, the Cross-Scale Feature Fusion Module (CCFM) was introduced to optimize the neck structure and enhance the adaptability of the model to multi-scale foam and the ability of small target detection. Secondly, the Mixed Local Channel Attention (MLCA) mechanism was embedded, and the channel information, spatial information and local information were integrated to improve the expression ability of the network. Finally, the Unified-IoU loss function was introduced to enable the model to dynamically shift attention from the low-quality prediction box to the high-quality prediction box, balancing training speed and detection accuracy. The experimental results show that compared with the traditional watershed algorithm and Mask R-CNN based on deep learning, the proposed method has significant advantages in segmentation accuracy and real-time performance. Compared with the benchmark model YOLOv11n-seg, the optimized model improves the accuracy of mAP@ 0.5 and mAP@ 0.5∶0.95 by 1.5% and 5.3%, respectively, the recall rate increases by 5.1%, the amount of computation decreases by 11.8%, and the number of parameters decreases by 32.1%. This method has a faster detection speed and higher detection accuracy, which can meet the needs of accurate extraction of flotation foam size.
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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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