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A driver fatigue detection method based on parallel short-term facial features is proposed to achieve faster and more accurate fatigue warning. The method utilizes the YOLOv7-MCW object detection network, which incorporates the MicroNet module, CA attention mechanism, and Wise-IoU loss function, to extract short-term facial features of the driver’s face. The parallel Informer temporal prediction network is then used to integrate the spatiotemporal information obtained from the YOLOv7-MCW object detection network, enabling the detection and warning of driver fatigue. The results demonstrate that the YOLOv7-MCW-Informer model achieves accuracy rates of 97.50% and 94.48% on the publicly available datasets UTA-RLDD and NTHU-DDD, respectively, with a single-frame detection time reduced to 28 ms, proving the excellent real-time fatigue detection performance of the model.
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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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