# A Lightweight Attention-Based Network towards Distracted Driving Behavior Recognition
## 4 Experimental Results
### Performance Evaluation of LWANet
#### 4.4.4 Model Comparison and Discussion  
In this study, we mainly focus on three problems. The first problem is that most trained models from classic networks cannot be deployed on resource-limited edge devices due to the high model complexity and computation cost. The second problem is that most lightweight networks are with relatively low accuracy. The third problem is that for most methods, a jitter of light or unnatural illumination condition may cause a wrong prediction. Towards these problems, we replace standard convolution with the depthwise separable convolution, optimize the network structure, introduce an IRAM attention mechanism, and utilize subtract mean filter as part of image preprocessing.  
In most papers, the proposed methods cannot balance the above three problems. In the past several decades, researchers have developed many techniques for deep learning-based distracted driving behavior recognition. They have improved a bit of accuracy, but the overall performance is still not desirable. Researchers have realized that computation cost is a bottleneck for model edge-oriented migration. They have reduced the model complexity significantly. However, for most vehicles, the onboard devices have much weaker microcontrollers than mobile phones. A trained model with a file size of more than 5 MB will result in a very low FPS on a mobile phone. Most proposed methods may not be able to run in real-time on a real vehicle device. To comparatively present the performance of LWANet, we compare our network with other state-of-the-art approaches and summarize the results in Table 7.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Author** & **Year** & **Network Baseline** & **Attention Module** & **Involved Dataset** & **Accuracy** & **Trainable Parameters** \\ \hline \multirow{4}{*}{[PERSON] et al. [47]} & \multirow{4}{*}{2016} & VGG16 & & & 90.2\% & \multirow{4}{*}{140 M} \\ \cline{2-6}  & & VGG-GAP & & & 91.3\% & \\ \cline{2-6}  & & Ensemble VGG16 and & & & 92.6\% & \\ \cline{2-6}  & & VGG-GAP & & & 96.8\% & \\ \hline \multirow{2}{*}{[PERSON] et al. [48]} & \multirow{2}{*}{2017} & AlexNet + Softmax & & \multirow{2}{*}{No} & \multirow{2}{*}{SF3D} & 96.8\% & \multirow{2}{*}{63.2 M} \\ \cline{2-6}  & & AlexNet + Triplet Loss & & & 98.7\% & \\ \hline \multirow{2}{*}{[PERSON] et al. [49]} & \multirow{2}{*}{2017} & ResNet & & \multirow{2}{*}{No} & \multirow{2}{*}{SF3D} & 85\% & 60 M \\ \cline{2-6}  & & AlexNet & & & 93.65\% & 62 M \\ \cline{2-6}  & & Inception V3 & & & 95.17\% & 24 M \\ \cline{2-6}  & & Majority Voting & & \multirow{2}{*}{No} & \multirow{2}{*}{AUC2D} & 95.77\% & 120 M \\ \cline{2-6}  & & Ensemble & & & 95.98\% & 120 M \\ \hline \multirow{2}{*}{[PERSON] et al. [50]} & \multirow{2}{*}{2018} & DenseNet & & \multirow{2}{*}{No} & \multirow{2}{*}{AUC2D} & 94.2\% & 8.06 M \\ \cline{2-6}  & & VGG16 & & & 94.44\% & 140 M \\ \cline{2-6}  & & VGG16 + Regularization & & & 96.31\% & 140 M \\ \cline{2-6}  & & Modified VGG16 & & & 95.54\% & 15 M \\ \hline \hline \end{tabular}
\end{table}
Table 7: A listed comparison of LWANet with other approaches.  
Among these approaches, [PERSON] et al. [54] is the only one focusing on lightweight network development. Compared to their work, we achieve a similarly competitive accuracy with only 1.2 M parameters and the aid of an attention mechanism. For some self-collected datasets which are not publicly available, we cannot further test the performance of our network.  
Additionally, it should be noted that LWANet works as a general solution towards image classification problems in other fields, including but not limited to agriculture, emotion, and traffic. Based on the lightweight design principle, LWANet has the potential to fit any other datasets and output a model with small file size. It works as an attempt towards the future development of edge-oriented deep learning.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline