Fig 1.
Schematic diagram of fatigue level classification.
Fig 2.
Schematic diagram of Mini-Xception’s network structure.
Fig 3.
Schematic comparison of normal convolution and depth-separable convolution.
Fig 4.
Model structure for facial fatigue expression recognition in the face.
Fig 5.
Novel human eye fatigue detection model structure.
Fig 6.
Network architecture of DCNN and DNN.
Fig 7.
Schematic diagram of DCNN-DNN fusion feature processing.
Fig 8.
Model performance test results with different parameter settings.
Fig 9.
Improved ablation test results for the Mini-Xception network.
Fig 10.
Test image display.
Fig 11.
Test AP results of different models for 8 graphs.
Fig 12.
Average runtime test results for different models.
Table 1.
Experimental environment and parameters.
Table 2.
Indicator test results for the same type of model.
Table 3.
Fifty-fold cross-validation results for randomized test images.
Table 4.
Accuracy test results of model detection with different data volumes.