# C1 DR-Net: Channel-Time Dense Residual Network for Detecting Crew Overboard Behavior
## 1 Introduction
It employs an enhanced YOLOv4 for human detection, utilizes DeeplabV3+ for water surface segmentation, and determines whether the target is in contact with the water surface. [PERSON] introduces a YOLO-based target detection algorithm using infrared images [11]. It streamlines the backbone network and incorporates an attention mechanism to enhance the success rate and efficiency of detecting small targets. The image-based overboard detection methods overlook the influence of overboard behavior coherence on detection accuracy. Meanwhile, factors such as light scattering and water surface ripples can also reduce the accuracy of overboard detection, leading to false and missed detections. Researchers utilize surveillance video frame sequences to design crew overboard behavior detection algorithms. Traditional models for behavior detection rely on manually designed features with poor generalization. Deep learning-based behavior detection models can effectively capture abstract features by learning massive amounts of data [12], which improves the generalization performance of the models in diverse scenarios [13]. A two-stream convolutional neural network that acquires features from RGB image and optical flow is proposed for behavior detection [14]. A Temporal Segment Network (TSN) is introduced to address the deficiency in long-term temporal modeling in dual-stream networks [15]. The dual-stream-based detection method can effectively extract the temporal and spatial features of the behavior. However, this method has difficulties in extracting the optical flow information of the video and requires a large amount of resources, which cannot meet the real-time requirements of crew overboard detection. [PERSON] et al. utilized three-dimensional convolutional (C3D) neural network to directly extract the temporal and spatial features of the behavior [16]. [PERSON] and [PERSON] proposed an Inflated 3D Convolutional Neural Network (I3D) [17], which was pre-trained on large-scale datasets and they fine-tuned the model to adapt to a specific behavioral detection task. [PERSON] et al. combined 3D convolution with ResNet to increase the network depth and improve the accuracy of behavior detection [18]. [PERSON] et al. proposed a Pseudo-3D Residual Network (P3D Resnet) to reduce the computational load of 3D convolutional networks [19]. These methods can effectively solve the temporal-spatial modeling problem and improve the accuracy of men overboard behavior detection, but fail to effectively distinguish behavioral features and background information. [PERSON] et al. introduced a channel attention model known as Squeeze-and-Excitation Networks (SENet) to augment the network's attention to different channel features [20]. [PERSON] et al. combined channel attention with spatial attention to enhance the model's focus on crucial channels and regions [21]. In the video-based monitoring system for crew behavior, the surveillance camera is positioned at the edge of the upper deck of the ship. It oversees the peripheral railing area of the ship from a high monitoring perspective. Inclement weather such as storms, heavy surf, and fog can reduce the clarity of video images. Meanwhile, the video image information acquired by the camera mostly contains the back and side of the crew, which has fewer usable features as the crew pose is obscured by the ship and the equipment on board. The pose information is completely lost due to the obstruction of the ship's hull when the crew falls over the side of the ship. The limited intelligent computing resources of ships exacerbate the difficulty of designing high-performance algorithms. From the above analysis, we can conclude that the crew overboard behavior has fewer available features and shorter durations. These characteristics increase the difficulty of abnormal behavior feature extraction and reduce the accuracy of detection. The proposed behavior detection algorithms have an imbalance in accuracy in real-time, which makes it difficult to meet the demand for crew overboard behavioral detection algorithms.