# C1 DR-Net: Channel-Time Dense Residual Network for Detecting Crew Overboard Behavior
## 4 Experiments
### Main Results  
The method proposed in this paper differs from the concepts of the other man-overboard detection algorithms described in the introduction, and there are no relevant datasets available for comparative experiments. While the conventional algorithms described in our manuscript swiftly detect crew overboard situations and issue alarms, they often do so after the crew member has already fallen into the water. In contrast, CTDR-Net monitors crew behavior near the ship's outer railing, enabling the detection of tendencies or actions indicating a potential overboard situation before the crew members fall, which can enhance the timeliness of early warning. A comparison experiment was conducted between the method proposed in this paper and several mainstream action recognition models, which include P3D-63, ResNet-50, DenseNet-65, and R (2 + 1) d-50, to assess the performance of the proposed method. The detailed results, presented in Table 2, indicate the following:  
1. Among the five action recognition network models, DenseNet-65 exhibits the lowest accuracy at 91.7%. Compared with this, the proposed method in this paper achieves a 5.2% improvement in accuracy, with the highest detection accuracy. The DR-Net network combines the advantages of dense blocks, residual blocks, and the CTAM in the feature extraction stage, resulting in a stronger feature extraction performance. The LeakyReLU activation function is used to enhance the generalization ability of the network to achieve preferable detection accuracy. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Methods** & **Accuracy (\%)** & **FLOPs (Gi)** & \
\begin{tabular}{c} **Parameters** \\ **(Mi)** \\ \end{tabular} &
\begin{tabular}{c} **FPR** \\ **(\%)** \\ \end{tabular} & **FNR (\%)** \\ \hline YOLO\_WA [(9] & 86.9 & - & - & - & - \\ Improve YOLOv4 [(11] & 98.68 & - & - & - & - \\ P3D-63 & 94.1 & 17.37 & 20.53 & 7.75 & 4.11 \\ ResNet-50 & 93.8 & 57.17 & 39.39 & 6.34 & 6.16 \\ DenseNet-65 & 91.7 & 58.75 & 4.51 & 9.15 & 7.53 \\ R (2 + 1) d-50 & 94.1 & 61.05 & 39.41 & 8.45 & 3.42 \\ Ours & 96.9 & 51.81 & 39.89 & 2.82 & 2.74 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparative experiment. Figure 6: Screenshots of videos of normal behavior and detection results. 2. The parameters in the method proposed in this paper (39.89 Mi) are comparable to ResNet-50 (39.39 Mi) and R (2 + 1) d-50 (39.41 Mi), and higher than that of DenseNet-65 (4.51 Mi). The increase in the parameters is reasonable, considering the substantial improvement in accuracy. The FLOPs of the method in this paper (51.81 Gi) surpasses P3D-63 (17.37 Gi) but is lower than the other three models, which can achieve high accuracy in detecting crew overboard behavior with relatively low computational effort. 3. The method proposed in this paper demonstrates significantly a lower false positive rate (2.82%) and false negative rate (2.74%) compared to other algorithms. The detection results shown in Figure 7 further underscore the effectiveness of our algorithm in identifying overboard behavior. Figure 8 presents two instances illustrating false negatives and false positives in our algorithm. In the (a) video screenshot, individuals are heavily obscured and the algorithm fails to extract sufficient valid features, resulting in a false negative. Meanwhile, in the (b) video screenshot, the low image quality and extremely low pixel resolution cause the algorithm to extract incorrect features, leading to a false positive. These two examples show that, when the person is heavily occluded or the picture quality pixels are very low, this can result in extremely limited effective features that can be extracted. In such situations, the algorithms in this paper may not be able to perform effective detection.