International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 11 Issue: 09 | Sep 2024
p-ISSN: 2395-0072
www.irjet.net
MAN-OVERBOARD EXPOSURE SYSTEM AND ADVANCED SAFETY (AI) TOPOGRAPHIES ENDURANCE ON ON-BOARD VESSEL Mugesh Palanivel1, Venkatesan Sadaiyan2, A C Mariappan3, G Peter Packiaraj4 1,2Final Year Marine Cadet, PSN CET, Tirunelveli, Tamil Nadu
3,4Assistant Professor, Dept. of Marine Engineering, PSN CET, Tirunelveli, Tamil Nadu
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ABSTRACT The Man Overboard (MOB) sensing system is a critical safety feature for maritime operations, designed to enhance the safety of crew and passengers on ships. This paper presents a novel AI-driven MOB detection system that leverages advanced sensor technologies and machine learning rule to automatically detect and respond to overboard incidents in real time. The system integrates multiple data sources, including high-resolution cameras, thermal imaging, and wearable devices, to monitor and analyze human activity on the ship's deck. By employing computer vision and anomaly detection techniques, the AI system can accurately identify and differentiate between normal activities and potential MOB events. Upon detecting an overboard incident, the system triggers immediate alerts, providing the crew with the exact location of the event and initiating automated emergency responses, such as adjusting the ship's course and deploying rescue equipment. This AIbased approach significantly reduces response time, improves the chances of a successful rescue, and minimizes the risk of human error. The paper also discusses the challenges of implementing such a system, including environmental variability, false positives, and privacy concerns, and proposes solutions to address these issues. The proposed MOB detection system represents a significant advancement in maritime safety.
Fig :1
2. AI-BASED SAFETY SYSTEM Advancements in artificial intelligence (AI) and sensor technologies offer new possibilities for enhancing maritime safety. AI-driven systems can process large volumes of data from various sensors in real time, enabling the automatic detection of MOB incidents without the need for constant human monitoring. This paper presents an AI-based MOB detection system designed to improve the accuracy and speed of incident detection and response on ships.
KEY WORDS: MOB, Detection, Tracking, ATM, SAS.
1. INTRODUCTION
The proposed system integrates multiple data sources, including video feeds from high-resolution and infrared cameras, wearable devices, and radar or LiDAR sensors. By applying machine learning techniques, the system can recognize and analyze patterns of human activity on the ship's deck, distinguishing between normal movements and those indicative of a potential overboard incident. In the event of detecting a MOB situation, the system triggers immediate alarms, alerts the crew with precise information about the location of the incident, and can even initiate automated rescue operations.
Man overboard (MOB) incidents are one of the most critical emergencies that can occur on a ship, often leading to life-threatening situations. Traditional MOB detection relies heavily on the vigilance of crew members or passengers to notice and report the incident, a process that is highly susceptible to human error, especially in low visibility or high-stress conditions. Delays in detection and response can drastically reduce the chances of a successful rescue, making it essential to explore more reliable and efficient methods.
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