多特征融合的驾驶员疲劳状态检测方法
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方浩杰,董红召,林少轩,罗建宇,方勇
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Driver fatigue state detection method based on multi-feature fusion
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Hao-jie FANG,Hong-zhao DONG,Shao-xuan LIN,Jian-yu LUO,Yong FANG
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| 表 1 不同模型的性能比较 |
| Tab.1 Performance comparison of different models |
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| 模型 | M/106 | FLOPs/109 | AP | mAP/% | | Um | Ym | Ce | Oe | Cm | Om | Ya | | Faster-RCNN [21] | 137.1 | 370.4 | 99.8 | 100 | 73.1 | 74.2 | 97.2 | 98.2 | 90.5 | 90.6 | | YOLOv3-tiny [22] | 8.7 | 13.0 | 99.0 | 99.0 | 90.3 | 91.0 | 94.1 | 94.3 | 94.0 | 94.5 | | SSD[23] | 26.3 | 62.8 | 99.5 | 99.8 | 84.2 | 90.0 | 89.8 | 95.4 | 98.0 | 93.8 | | YOLOv5-S[24] | 7.0 | 15.9 | 99.0 | 99.3 | 91.8 | 92.5 | 94.5 | 95.0 | 94.5 | 95.2 | | YOLOv5-S+Detection layer | 8.2 | 28.3 | 98.7 | 99.1 | 94.8 | 95.7 | 97.3 | 98.8 | 97.6 | 97.5 | | YOLOv5-S+BiFPN | 7.1 | 16.6 | 99.2 | 99.4 | 93.2 | 94.4 | 96.3 | 97.3 | 98.0 | 96.8 | | YOLOv5-S+ Detection layer+BiFPN (本文模型) | 9.3 | 29.9 | 99.5 | 99.5 | 98.6 | 99.5 | 99.5 | 99.5 | 99.5 | 99.4 |
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