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In the process of driving a vehicle, the complex and changing environment inside the vehicle, the change of lighting conditions and the diversity of drivers’ behavioral postures affect the detection and recognition of abnormal driver behavior. To address this issue, this paper proposes a driver abnormal driving behavior detection algorithm based on contrast learning. The paper firstly considers driver’s driving behavior detection as a binary classification task, and utilizes a contrast learning approach to compare driver’s normal driving with abnormal driving samples and to improve the performance of the model by contrasting loss functions. Secondly, the depth images right ahead and above the driver serves as inputs to solve the problems of complex in-vehicle environment to change the light intensity and blind spots in viewpoint by providing the depth information of the driver. Finally, 3D convolution is introduced in the lightweight network MobileNetV2, and the operation of channel blending is added to the convolution layer of each bottleneck structure to improve the accuracy of recognition. Test results show that accuracy of the proposed algorithm reaches 94.18% in the Driver’s Abnormality Detection (DAD) dataset and ROC AUC reaches 0.962, which shows the effectiveness of the algorithm in driver’s abnormal behavior detection.
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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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