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http://dx.doi.org/10.14372/IEMEK.2022.17.5.281

Deep Learning based Distress Awareness System for Small Boat  

Chon, Haemyung (Kunsan National University)
Noh, Jackyou (Kunsan National University)
Publication Information
IEMEK Journal of Embedded Systems and Applications / v.17, no.5, 2022 , pp. 281-288 More about this Journal
Abstract
According to statistics conducted by the Korea Coast Guard, the number of accidents on small boats under 5 tons is increasing every year. This is because only a small number of people are on board. The previously developed maritime distress and safety systems are not well distributed because passengers must be equipped with additional remote equipment. The purpose of this study is to develop a distress awareness system that recognizes man over-board situations in real time. This study aims to present the part of the passenger tracking system among the small ship's distress awareness situational system that can generate passenger's location information in real time using deep learning based object detection and tracking technologies. The system consisted of the following steps. 1) the passenger location information is generated in the form of Bounding box using its detection model (YOLOv3). 2) Based on the Bounding box data, Deep SORT predicts the Bounding box's position in the next frame of the image with Kalman filter. 3) When the actual Bounding Box is created within the range predicted by Kalman-filter, Deep SORT repeats the process of recognizing it as the same object. 4) If the Bounding box deviates the ship's area or an error occurs in the number of tracking occupant, the system is decided the distress situation and issues an alert. This study is expected to complement the problems of existing technologies and ensure the safety of individuals aboard small boats.
Keywords
Distress awareness system; Small boat; YOLOv3; Deep SORT; Re-identification;
Citations & Related Records
Times Cited By KSCI : 3  (Citation Analysis)
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