# C1 DR-Net: Channel-Time Dense Residual Network for Detecting Crew Overboard Behavior
## 1 Introduction  
Marine disasters, such as wind surges, tsunamis, waves, ocean earthquakes, and underwater volcanoes, are occurring more frequently with global climate change in recent years, which has seriously affected maritime navigation safety and brought significant risks to maritime economic activities [1]. Crew overboard accidents occur frequently due to the impact of marine disasters and limitations in the working environment at sea, which cause substantial loss of life and property. A rapid and accurate method for detecting crew overboard can reduce warning time, improve rescue response efficiency, and reduce loss of life and property. The method has become an important aspect in improving the emergency response capabilities of marine disasters and the rescue efficiency of maritime accidents. Traditional overboard detection systems utilize various sensing devices such as pressure sensors, infrared sensors, and sound sensors to detect overboard incidents. [PERSON] et al. designed W-MEDS, a Wireless Sensor Network (WSN)-based system for swiftly detecting and locating individuals in man-overboard emergencies on ships [2]. W-MEDS uses real-time sensing by WSN nodes (temperature, humidity, acceleration) to trigger alarms and initiate rescue procedures through a control and discovery system. In order to enhance the notification speed and the accuracy of locating individuals who have fallen overboard, the feasibility of using wireless transmitters was investigated [3]. After conducting a literature review, the choice of technology fell on LoRa. Field tests were conducted in a maritime environment, revealing challenges in employing wireless technology for detecting and tracking overboard individuals on large cruise ships. These challenges are primarily attributed to the physical limitations of radio wave propagation in water. [PERSON] et al. proposed a real-time MOB alert, GPS tracking, and monitoring system [4]. Thesystem includes wearable sensors, remote LoRa access points, physical electronic fences, and three MOB detection methods: real-time notification via wearables, virtual electronic fence monitoring based on ship size, and instant notification triggered by a surrounding electronic fence. Laboratory and sea tests demonstrated the system's ability for quick MOB detection and notification, showcasing the ship's real-time MOB detection and prompt rescue capabilities. [PERSON] et al. proposes a joint time-frequency domain processing method for the detection of man-overboard signals [5]. It utilizes empirical modal decomposition to separate and detect multi-component signals for the purpose of effectively detecting men overboard. The traditional overboard detection systems focus on deploying sensing devices to activate the overboard alarm. The forms of data collection and transmission are easily affected by and interfered with by external environments. These factors result in high system maintenance costs [6]. Crew overboard detection technology is progressively evolving towards automation and intelligence, driven by the advancement of mobile communication technology and artificial intelligence [7]. The intelligent detection of men overboard with computer vision and image processing techniques is a current research focus. The PSPNet (Pyramid Scene Parsing Network) deep learning model is proposed to achieve pedestrian overboard detection [8]. It uses the principle of motion detection to calculate the distance between the pedestrian center of mass and the contour of the lake, detecting overboard incidents by judging the distance. The YOLO-WA (you only look once-water area) algorithm is proposed for target detection in waterside environments [9]. It selects activation functions, introduces attention mechanisms, and replaces loss functions to improve the accuracy of human recognition in aquatic settings. [PERSON] introduces a man overboard detection algorithm applicable to the surrounding waters of pump ships [10].