• Information and Communications Magazine
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• v.34 no.6
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• pp.51-57
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• 2017

본고에서는 사물 인터넷 기술을 기반으로 한 실종자 탐색 기술과 서비스의 현황을 살펴보고 스마트폰 기반 실종자 탐색 서비스의 필요성과 시스템 구성 및 구현 방안에 대해 살펴 본다. 이 서비스는 실종자가 소지한 발신기 신호를 인지하는 수신 인프라의 종류에 따라 실종자 탐지 서비스의 비용, 실효성 등에 큰 영향을 받는다. 수신 인프라로 이동통신망, LoRa 망, 블루투스 망, 그리고 스마트폰을 사용하는 각 방안의 구조와 장단점을 살펴 본다.

• Journal of Korea Society of Industrial Information Systems
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• v.25 no.3
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• pp.53-59
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• 2020

This paper proposes color segmentation for detecting missing persons in a smart lighting system using radar and camera sensors. Recently, smart lighting systems built-in radar and cameras have been efficient in saving energy and searching for missing persons, simultaneously. In smart lighting systems, radar detects moving objects and then the lights turn on and camera records. The video recorded is useful to find out missing persons. The color of their clothes worn in missing persons is one of critical hints to look for missing persons. Therefore, color segmentation is an effective means for detecting the color of their clothes. In this paper, during the color segmentation step, the ROI(Region of interest) setting based on the size of an object is applied and the background is reduced. According to experimental results, the color segmentation has good accuracy of more than 97%.

• Smart Media Journal
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• v.12 no.9
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• pp.159-170
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• 2023

It takes a lot of time and manpower to search for the missing. As part of the solution, a missing person search AI system was implemented using a YOLO-based model. In order to train object detection models, the model was learned by collecting recognition images (road fixation) of drone mobile objects from AI-Hub. Additional mountainous terrain datasets were also collected to evaluate performance in training datasets and other environments. In order to optimize the missing person search AI system, performance evaluation based on model size and hyperparameters and additional performance evaluation for concerns about overfitting were conducted. As a result of performance evaluation, it was confirmed that the YOLOv5-L model showed excellent performance, and the performance of the model was further improved by applying data augmentation techniques. Since then, the web service has been applied with the YOLOv5-L model that applies data augmentation techniques to increase the efficiency of searching for missing people.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.12
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• pp.427-436
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• 2016

Due to disasters such as earthquakes and landslides in urban areas, persons have been buried inside collapsed buildings and structures. Rescuers have mainly utilized detection equipment by applying sound, video and electric waves, but these are expensive and due to the directional approaches onto the collapsed site, secondary collapse risk can arise. In addition, due to poor utilization of such equipment, new human detection technology with quick and high reliability has not been utilized. To address these issues, this study develops a wireless signal-based human detection module that can be loaded into an Unmanned Aerial Vehicle (UAV). The human detection module searches for the 3D location for buried persons by collecting Wi-Fi signal and barometer sensors data transmitted from the mobile phones. This module can gain diverse information from mobile phones for buried persons in real time. We present a development framework of the module that provides 3D location data with more reliable information by delivering the collected data into a local computer in the ground. This study verified the application feasibility of the developed module in a real collapsed area. Therefore, it is expected that these results can be used as a core technology for the quick detection of buried persons' location and for relieving them after disasters that induce building collapses.

• Proceedings of the Korean Society of Disaster Information Conference
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• 2023.11a
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• pp.297-298
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• 2023

해상 사고 시 지상에서 통상적으로 사용하는 빛이나 전파가 투과하지 못하는 수중에서 수중 음향을 이용해 수백 m 이상의 원거리에서 실종자나 화물의 위치를 추적하기 위해 요구되는 조건들을 분석하고 이를 실증하기 위해 인체에 부착 가능한 크기의 소형 수중 음향 송신기와 이를 찾기 위해 수색 작전에 사용하는 수신기가 제작됐다. 이를 사용해 서해 상에서 1km 거리에서 음향 신호 수신을 확인했고 실제 현장에서 적용되기 위해 필요한 개선점들을 정리했다.

• Journal of the Korean Society of Marine Environment & Safety
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• v.28 no.7
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• pp.1120-1128
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• 2022

In the event of a marine accident, the longer the exposure time to the sea increases, the faster the chance of survival decreases. However, because the search area of the sea is extremely wide compared to that of land, marine object detection technology based on the sensor mounted on a satellite or an aircraft must be applied rather than ship for an efficient search. The purpose of this study was to rapidly detect an object in the ocean using a hyperspectral image sensor mounted on an aircraft. The image captured by this sensor has a spatial resolution of 8,241 × 1,024, and is a large-capacity data comprising 127 spectra and a resolution of 0.7 m per pixel. In this study, a marine object detection model was developed that combines a seawater identification algorithm using DBSCAN and a density-based land removal algorithm to rapidly analyze large data. When the developed detection model was applied to the hyperspectral image, the performance of analyzing a sea area of about 5 km² within 100 s was confirmed. In addition, to evaluate the detection accuracy of the developed model, hyperspectral images of the Mokpo, Gunsan, and Yeosu regions were taken using an aircraft. As a result, ships in the experimental image could be detected with an accuracy of 90 %. The technology developed in this study is expected to be utilized as important information to support the search and rescue activities of small ships and human life.