# A Data Augmentation Approach to Distracted Driving Detection
## 3 Results
### Results for CNN Classification  
In the experiment, the dataset of AUC and DOA were both 12,997 images of training set and 4331 images of test set. The image classification model AlexNet, InceptionV4 and Xception were used to train with image shape of 224 \(\times\) 224 \(\times\) 3, the learning rate of 0.001, batch size of 32, and epoch of 100. The top-1 accuracy was selected to evaluate the performance of the models. We performed 3 rounds of verification. Table 2 summarizes the test results for loss and accuracy of three different convolutional network models: AlexNet, InceptionV4, and Xception.  
As can be seen from Table 2, the test top-1 accuracy of the AlexNet, InceptionV4 and Xception on the AUC dataset are 0.9314, 0.9506 and 0.9531, respectively, and the test results on the DOA dataset are 0.9386, 0.9572 and 0.9655, which means the DOA dataset has higher detection accuracy and lower loss value than the original AUC dataset.  
Figure 5 shows the change for loss and accuracy of each method in each epoch stage. When the epoch is 10, the loss and accuracy of the DOA dataset with the Xception model begin to stabilize, and when the epoch is 14, the original AUC dataset loss and accuracy with the Xception model begin to stabilize. Moreover, The loss values in the DOA-based results are lower than original AUC dataset. It can be seen from the testing loss and accuracy curves with varying epochs, the loss of DOA dataset corresponding to the key areas of driving behavior converges faster than the original AUC dataset, and the detection accuracy rises faster too.  
Finally, the DOA training set obtained through data augmentation and the original AUC training set were merged to expand the dataset. The final classification accuracy is shown in Table 3. Among the three classification models, the baseline with Xception has the smallest fluctuation, the lowest loss result, and the highest accuracy, which is the most suitable for the benchmark model of this classification. For more evaluation, Figure 6 is the confusion matrix for the classification results of the ten distracting behaviors with Xception. Using the given confusion matrix, one can check that many categories can  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Source** & **Loss** & **Top-1 Acc.** \\ \hline \multirow{2}{*}{AlexNet} & AUC & 0.3753 & 0.9314 \\  & DOA & 0.3402 & 0.9386 \\ \hline \multirow{2}{*}{InceptionV4} & AUC & 0.3041 & 0.9506 \\  & DOA & 0.2771 & 0.9572 \\ \hline \multirow{2}{*}{Xception} & AUC & 0.2320 & 0.9531 \\  & DOA & 0.2156 & 0.9655 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Image classification test result with AUC and DOA dataset.  
Figure 5: Accuracy and loss of image classification result. (**a**) the loss of each method in each epoch stage; (**b**) the accuracy of each method in each epoch stage.  
easily be mistaken for (c0) \"safe driving\". Moreover, the most confusing operation is (c8) \"hair and makeup\". It may be due to the position of \"hands on the wheel\" in both classes.  
Our distracted driver detection result was compared with earlier methods in the literature. Compared with some early methods, our method can be applied to the preprocessing stage. We achieve the best accuracy than earlier methods as shown in Table 4. Among them, the top-1 accuracy of our module based on Xception is finally 0.9697, which is 1.66% higher than the classification accuracy of origin AUC dataset.