# C1 DR-Net: Channel-Time Dense Residual Network for Detecting Crew Overboard Behavior
## 4 Experiments
### Ablation Study  
The DR-Net comprises two dense blocks and two residual blocks. In this section, we employ DenseNet-65 as the baseline, which consists of four dense blocks and incorporates pseudo-convolution internally. Five experimental groups are designed for ablation experiments to evaluate the feasibility and effectiveness of DR-Net structure, LeakyReLU activation function, and CTAM. The experiments are detailed in Table 1.  
The experimental results show that, (1) compared with the baseline model, the accuracy of the DR-Net was improved by 6.3%. This is because the DR-Net employs dense blocks for extracting shallow features and residual blocks for extracting deep features, which reduces overfitting and enhances the model's feature extraction capabilities. (2) The DR-Net (LeakyReLU) model exhibits a 1% accuracy improvement over the DR-Net model using the ReLU activation function, which indicates that the LeakyReLU activation function enhances the model's nonlinear capabilities and expressive power. (3) The accuracy of the model improved by 0.3% and 0.4% with the addition of CTAM compared to the DR-Net and DR-Net (LeakyReLU) models without the attention mechanisms. The improved accuracy affirms that the CTAM enhances attention to critical channel information at each time step, strengthens the model's feature extraction capabilities, and improve its accuracy. (4) The DR-Net network with the LeakyReLU activation function and the introduction of the CTAM achieves the highest accuracy at 96.9%.  
Figure 6 displays screenshots from two different normal videos. In Video 1, camera shake occurred during the recording, resulting in blurred image quality and partial loss of behavioral features. Additionally, some features of individuals are obscured by background equipment. In Video 2, the coverage area of background devices is larger, leading to the more extensive obscuration of individual features. For the detection of the two normal behaviors shown in Figure 6, DR-Net (LeakyReLU) + CTAM identifies them as normal activities (left), while DR-Net (LeakyReLU) detects them as falling behavior (right). This further underscores that the addition of CTAM is more effective in focusing on the behavior itself, reducing errors caused by background jitter and device obstruction.  
The experiment results show that the model has a moderate increase in time-space cost, with a significant improvement in accuracy of 6.3%, and that the overall performance has been substantially enhanced. The addition of the LeakyReLU activation function and the CTAM results in very modest changes in the model's FLOPs and parameters, which has a negligible impact on hardware performance.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & **Dense Block** & **Residual Block** & **LeakyReLU** & **CTAM** & **Accuracy (\%)** & **FLOPs (Mi)** & **Parameters (Mi)** \\ \hline Baseline & \(\surd\) & \(\times\) & \(\times\) & \(\times\) & 89.2 & 34,448.19 & 23.97 \\ DR-Net & \(\surd\) & \(\surd\) & \(\times\) & \(\times\) & 95.5 & 51,809.40 & 39.04 \\ DR-Net (LeakyReLU) & \(\surd\) & \(\surd\) & \(\surd\) & \(\times\) & 96.5 & 51,809.40 & 39.07 \\ DR-Net + CTAM & \(\surd\) & \(\surd\) & \(\times\) & \(\surd\) & 95.8 & 51,811.17 & 39.89 \\ DR-Net (LeakyReLU) + & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) & 96.9 & 51,811.17 & 39.89 \\ CTAM & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) & 96.9 & 51,811.17 & 39.89 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Ablation experiment.