Full
Outline
Aiming at the problems of low efficiency and difficulty in applying traditional fire detection algorithms to embedded devices with limited resources, a lightweight fire detection algorithm based on improved YOLOv8n was proposed. Firstly, in order to greatly reduce the number of model parameters, the C2f module in the YOLOv8n model was changed to the C2fGhost module. Secondly, in order to prevent the loss of important features after the lightweight of the model, strengthen the feature extraction ability and reduce the interference of irrelevant information in the image, the EMA attention mechanism was added to the Backbone part of the YOLOv8n model. Finally, in order to improve the generalization ability of the model, the CIoU loss function in the YOLOv8n model was replaced by WIoU. The experimental results show that the parameters of the improved model decrease by 30.1%, mAP50−95 increases by 0.6 percentage points, and the accuracy rate decreases by only 0.2 percentage points. The improved model can detect the underground fire in real time and efficiently, and the detection algorithm has reference value for mine safety management.
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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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