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Outline
Aiming at the common issues of insufficient scene adaptability and inadequate modeling of temporal feature dependencies in existing visual fatigue detection methods, this paper proposes an intelligent rail driver fatigue detection algorithm based on facial multi-features and Bi-LSTM. First, the algorithm uses Dlib to locate the driver's facial keypoints and calculates five fatigue feature parameters, including the Eye Aspect Ratio (EAR), Mouth Aspect Ratio (MAR), and head pose angles. It also proposes the frequency of longitudinal head scanning behavior for distraction detection. Second, the Bi-LSTM network is employed to establish temporal dependencies and complete the determination of the driving state. Third, temporal attention is introduced to enhance the representation capability of key features, improving the overall performance of the algorithm. Finally, the research results show that, compared to traditional models, the proposed method achieves an accuracy of 94.24% and improves the F1-Score from 0.8324 to 0.9027, validating the effectiveness and feasibility of the proposed approach. The findings can provide a reference for public transportation safety assurance.
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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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