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With regard to the driving environment and safety of sanitation vehicle drivers, this paper proposes a driver fatigue detection method based on an enhanced YOLOv8n algorithm. Specifically, FasterNet is employed to replace the backbone network of the YOLOv8 object detection algorithm, resulting in the design of a lightweight FasterNet-YOLO network model. To preserve critical feature information from the input feature map, Squeeze-and-Excitation (SE) modules are integrated into the backbone network, while Convolutional Block Attention Modules (CBAM) are added to the neck network. Additionally, the Zero-DCE++ algorithm is introduced to enhance the brightness of video streams captured by cameras, addressing the issue of insufficient brightness in the driver’s face that hinders accurate detection. Experimental results demonstrate that the proposed method achieves an average precision of 98% (mAP@0.5) at an intersection over union ratio of 0.5, with an average inference time per frame reduced to 6.95 ms. This approach can effectively monitor the driver’s fatigue state in real-time under varying lighting conditions.
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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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