# Driver Fatigue Detection Based on Residual Channel Attention Network and Head Pose Estimation
## 4 Experiments and Results
### Performance of the RCAN  
To prove the superiority of the RCAN in driving fatigue detection, we compared the RCA \(\alpha\) N with other classical CNN structures, i.e., ResNetXt-50 [27], InceptionV4 [28], and EffcientNet [24], in four datasets. The results are shown in Table 3. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dataset** & **Training Object** & **Test Data** & **Method** & **Accuracy (\%)** \\ \hline \multirow{8}{*}{_SDF_} & Eyes & 3642 & ResNetXt-50 & 97.968 \\  & & & InceptionV4 & 97.831 \\  & & & EfficientNet & 98.325 \\  & & & RCAN(no attention) & 96.541 \\  & & & RCAN (CBAM) & 98.465 \\  & & & RCAN & 98.962 \\  & & & ResNetXt-50 & 97.595 \\  & & & InceptionV4 & 97.697 \\  & & & EfficientNet & 98.464 \\  & & & RCAN (no attention) & 94.417 \\  & & & RCAN(CBAM) & 98.327 \\  & & & RCAN & 98.516 \\ \hline \multirow{8}{*}{DROZY} & Eyes & 782 & ResNetXt-50 & 98.593 \\  & & & InceptionV4 & 98.593 \\  & & & EfficientNet & 98.043 \\  & & & Recon (no attention) & 96.304 \\  & & & RCAN (CBAM) & 98.261 \\  & & & RCAN & 98.478 \\ \hline \multirow{8}{*}{YawDD} & Eyes & 1102 & ResNetXt-50 & 98.457 \\  & & & InceptionV4 & 98.276 \\  & & & EfficientNet & 98.548 \\  & & & RCAN (no attention) & 95.531 \\  & & & RCAN (CBAM) & 98.557 \\  & & & RCAN & 99.002 \\  & & & ResNetXt-50 & 98.172 \\  & & & InceptionV4 & 98.477 \\  & & & EfficientNet & 98.782 \\  & & & RCAN (no attention) & 94.188 \\  & & & RCAN(CBAM) & 98.438 \\  & & & RCAN & 98.678 \\ \hline \multirow{8}{*}{CEW} & Eyes & 969 & ResNetXt-50 & 98.555 \\  & & & Inception & 98.967 \\ \cline{1-1}  & & & EfficientNet & 98.762 \\ \cline{1-1}  & & & RCAN (no attention) & 97.751 \\ \cline{1-1}  & & & RCAN(CBAM) & 98.967 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Classification accuracy of the RCAN and different classical CNN structures on four datasets. In the CEW and DROZY datasets, the classification results of the RCAN and other CNNs are relatively close. Most human eye images of the CEW dataset are taken in the forward direction, which caused the data dispersion to be low and easy to classify. In SDF and YawDD, the classification accuracy of the RCAN is higher than that of other CNNs. More deeply, we use _Precision (p)_, _Recall_ (R), and _F-score_ to evaluate the classification performance of the RCAN. The equations of the above evaluation indicators are as follows:  
\[Precision=\frac{TP}{TP+FP} \tag{17}\]  
\[Recall=\frac{TP}{TP+FN} \tag{18}\]  
\[F\text{-}score=\frac{2\times Precision\times Recall}{Precision+Recall} \tag{19}\]  
where _TP_ is the number of closed eye/mouth images that are predicted to be correct by the model. In contrast, _FP_ is the number of closed eye/mouth images that are predicted to be false. _FN_ is the number of the actual open eye/mouth images mistakenly classified as closed eye/mouth images. _F-score_ is the harmonic average of precision and recall. The higher the _F-score_, the better the performance of the classification model. The performance of the RCAN in _TP_, _FP_, and _F-score_ in each dataset is shown in Table 4.  
Figure 6 shows the PR curves of the RCAN under four datasets. The larger the area wrapped by the PR curve, the better the classification performance. The experimental results demonstrate that the classification performance of the proposed RCAN on the eye state is better than on the mouth state. In the SDF dataset, the accuracy of the RCAN can reach 98.962% for eye state classification. Similarly, the accuracy of the RCAN can reach 98.516% in the classification of the mouth state. The RCAN also includes the channel attention module. To verify the effectiveness the channel attention module, we adopted the ablation strategy for experiment. We compared the original RCAN with the RCAN that deleted the channel attention module and the RCAN that fixed \(\alpha\) as 0.5. The RCAN that fixed \(\alpha\) as 0.5 is equivalent to the channel attention submodule of the CBAM [26]. Therefore, this study names this network structure as the RCAN (CBAM).