# Driving Fatigue Detection Based on the Combination of Multi-Branch 3D-CNN and Attention Mechanism
## 5 Discussion  
In this paper, we propose a driving fatigue detection model based on a 3D convolutional neural network combined with a channel attention mechanism module. The experiments are based on four datasets, and multiple sets of control experiments are  
Figure 11: Comparison of the accuracy of each behavior detection with or without frame aggregation strategy on the FDF dataset. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Research** & **Dataset** & **Method** & **Accuracy (\%)** \\ \hline [PERSON] et al. [34] & YawDD & Kalman + TLD + CNN & 92 \\ [PERSON] et al. [35] & YawDD & MC-KCF + blinking + yawning & 96.3 \\ [PERSON] et al. [36] & YawDD & MSR-Net & 98.45 \\ [PERSON] et al. [16] & YawDD & RCAN(CBAM) & 98.43 \\ [PERSON] et al. [14] & NTHU & Resnet + DHLSTM & 99.3\% \\ [PERSON] et al. [15] & Homemade & relLU-BiLSTM & 99.2\% \\ Ours & YawDD & 3D-SE-Net(D) & 97.34 \\  & YawDD & 3D-SE-Net(T) & **99.03** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison results of the proposed method and state-of-the-art methods. conducted for different lighting and occlusion conditions, with or without the attention mechanism module, and whether to use the frame aggregation strategy. It is compared with other current fatigue detection methods and achieves satisfactory detection accuracy. Unlike other methods, none of them take into account the leakage of driver's facial information. In the method proposed in this paper, we use the template fusion method to fuse the driver's face and finally output the simulated image of the driver's face through 3D simulation, which fundamentally avoids the problem of driver's facial privacy leakage. There are indeed some problems with 3D convolution. For the existing problems, we have given corresponding solutions in the process of model construction and experimentation:  
1. From the perspective of data format: The flaw of the 3D network lies in the problem of memory occupation, which makes it impossible to use the entire 3D image layer as input and must be cropped and cut into a series of 3D patches as input. The cropped blocks will limit the maximum receptive field that the network can achieve, resulting in the loss of certain global information. If the target to be segmented is itself much larger than the cropped blocks, it is difficult for the network to learn the overall structural information of the target. For this problem, we adopt the scheme of hardwired layer to introduce the information of five channels of grayscale, optical flow and gradient into the network, which encodes the prior knowledge of feature information and increases the richness of input information to reduce the impact of this information loss. 2. From the perspective of model training: Generally speaking, the number of parameters of 3D convolution is larger, so the 3D convolution we commonly use are not like 2D convolution. The 3D convolution is not down sampled at a high multiplicity, but at a lower multiplicity. However, due to the matching problem between the amount of data and the amount of model parameters, the 3D convolutional neural network may require more data to train, otherwise it may lead to overfitting. In response to this problem, our approach is to expand the dataset as much as possible. As shown in Figure 7, we have collected a large number of fatigue detection datasets and in addition we have collected and produced our own datasets under real vehicle conditions and trained on a large amount of data to improve the generalization performance of the model. If there are enough training samples and the computing power is strong enough, the model can quickly fit to the optimal encoding function. 3. From the perspective of problem context: Most of the deep learning-based driving fatigue detection methods process driver facial signals and the process of fatigue is usually a time-series problem.