• Journal of the Korea Society of Computer and Information
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• v.26 no.7
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• pp.19-27
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• 2021

In this paper, we propose a method for detecting characters in images and detecting facial regions, which consists of two tasks. First, we separate two different characters to detect the face position of the characters in the frame. For fast detection, we use You Only Look Once (YOLO), which finds faces in the image in real time, to extract the location of the face and mark them as object detection boxes. Second, we present three image processing methods to detect accurate face area based on object detection boxes. Each method uses HSV values extracted from the region estimated by the detection figure to detect the face region of the characters, and changes the size and shape of the detection figure to compare the accuracy of each method. Each face detection method is compared and analyzed with comparative data and image processing data for reliability verification. As a result, we achieved the highest accuracy of 87% when using the split rectangular method among circular, rectangular, and split rectangular methods.

• Korean Journal of Remote Sensing
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• v.38 no.5_2
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• pp.737-746
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• 2022

Utilizing weather independent SAR images along with machine learning based object detector is effective in robust vessel monitoring. While conventional SAR images often applied amplitude data from Single Look Complex, exploitation of polarimetric parameters acquired from multiple polarimetric SAR images was yet to be implemented to vessel detection utilizing machine learning. Hence, this study used four polarimetric parameters (H, p₁, DoP, DPRVI) retrieved from eigen-decomposition and two backscattering coefficients (γ₀, _VV, γ₀, _VH) from radiometric calibration; six bands in total were respectively exploited from 52 Sentinel-1 SAR images, accompanied by vessel training data extracted from AIS information which corresponds to acquisition time span of the SAR image. Evaluating different cases of combination, the use of polarimetric indexes along with amplitude values derived enhanced vessel detection performances than that of utilizing amplitude values exclusively.

• Journal of Korea Society of Industrial Information Systems
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• v.29 no.1
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• pp.77-89
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• 2024

This paper presents an unmanned store management system that can provide inventory management and theft prevention for displayed products using a small camera that can monitor the shelves of sold products in small and medium-sized stores. This system is a service solution that integrates object recognition, real-time communication, security management, access management, and mobile authentication. The proposed system uses a custom YOLOv5-x model to recognize objects on the display, measure quantities in real time, and support real-time data communication with servers through Raspberry Pie. In addition, the number of objects in the database and the object recognition results are compared to detect suspected theft situations and provide burial images at the time of theft. The proposed unmanned store solution is expected to improve the efficiency of small and medium-sized unmanned store operations and contribute to responding to theft.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.10 no.4
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• pp.91-97
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• 2010

This paper describes a development of an network based remote surveillance system using omni-directional mobile robot. the proposed surveillance system can control a mobile robot to move and examines the given place closely while the conventional surveillance system uses a fixed camera. The mobile robot in the proposed system has three omni-directional wheels to move to any given direction freely. We also developed the proposed system as robot services using Microsoft's MSRDS for a user to control the mobile robot and monitor the remote scene captured from the robot. Finally we verified the feasibility and effectiveness of the proposed system by conducting the remote operating the mobile robot and monitoring experiments in a networked environment. We also conducted a color based object detection and motion detection on image sequences acquired from a remote mobile robot in an another PC in a network environment.

• Geophysics and Geophysical Exploration
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• v.8 no.1
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• pp.97-103
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• 2005

Recent development of marine magnetic gradient systems, using arrays of sensors, has made it possible to survey large contaminated areas very quickly. However, underwater Unexploded Ordnances (UXO) can be moved by water currents. Because of this mobility, the cleanup process in such situations becomes dynamic rather than static. This implies that detection should occur in near real-time for successful remediation. Therefore, there is a need for a fast interpretation method to rapidly detect signatures of underwater objects in marine magnetic data. In this paper, we present a fast method for location and characterization of underwater UXOs. The approach utilises gradient interpretation techniques (analytic signal and Euler methods) to locate the objects precisely. Then, using an iterative linear least-squares technique, we obtain the magnetization characteristics of the sources. The approach was applied to a theoretical marine magnetic anomaly, with random errors, over a known source. We demonstrate the practical utility of the method using marine magnetic gradient data from Japan.

• Journal of Internet of Things and Convergence
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• v.6 no.1
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• pp.83-95
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• 2020

Being a dense and complex task of precisely detecting the weeds in practical crop field environment, previous approaches lack in terms of speed of processing image frames with accuracy. Although much of the attention has been given to classify the plants diseases but detecting crop weed issue remained in limelight. Previous approaches report to use fast algorithms but inference time is not even closer to real time, making them impractical solutions to be used in uncontrolled conditions. Therefore, we propose a detection model for the complex rice weed detection task. Experimental results show that inference time in our approach is reduced with a significant margin in weed detection task, making it practically deployable application in real conditions. The samples are collected at two different growth stages of rice and annotated manually

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• v.29 no.1
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• pp.273-277
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• 2005

In this paper, we study on adaptation of the kalman filter for FDS(fishery detection system) to protect and aquaculture farms. The FDS will detect a robbing vessel with real time and a variance of the position of fishing fields. The kalman filter for tracking system that can be detect and track the approaching object without mounting F-AIS(Fishery Automatic Identification System) is applied. Some simulation results for the acceleration object with white noise is showed and the possibility of adaptation for tracking system is discussed.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2006.06b
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• pp.227-232
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• 2006

In this paper, a Kalman filter application for GDSS(Group Digital Surveillance System) developed to protect an aquaculture farms is discussed GDSS is composed by a WIWAS(Watching, Identification, Warning, and Action System) and a FDS(Fishery Detection System) that will monitor incoming and outgoing vessels in the aquaculture farms. In the FDS, a tracking function to track vessels without F-AIS(Fishery Automatic Identification System) is needed and the Kalman filter is applied to track vessels around the aquaculture farms. Some simulation results for the multi-objects with white noise is presented and the adaptation possibility for tracking system is discussed.

• The Journal of the Acoustical Society of Korea
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• v.42 no.4
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• pp.370-376
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• 2023

In the underwater image, it is not clear to distinguish the shape of the target due to underwater noise and low resolution. In addition, as an input of deep learning, underwater images require pre-processing and segmentation must be preceded. Even after pre-processing, the target is not clear, and the performance of detection and identification by deep learning may not be high. Therefore, it is necessary to distinguish and clarify the target. In this study, the importance of target shadows is confirmed in underwater images, object detection and target area acquisition by shadows, and data containing only the shape of targets and shadows without underwater background are generated. We present the process of converting the shadow image into a 3-mode image in which the target is white, the shadow is black, and the background is gray. Through this, it is possible to provide an image that is clearly pre-processed and easily discriminated as an input of deep learning. In addition, if the image processing code using Open Source Computer Vision (OpenCV)Library was used for processing, the processing speed was also suitable for real-time processing.

• Journal of Intelligence and Information Systems
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• v.15 no.1
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• pp.51-64
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• 2009

A location tracking sensor such as GPS, INS, Radar, and optical equipments is used in tracking Maneuvering Targets with a multi-sensor, and such systems are used to track, detect, and control UAV, guided missile, and spaceship. Until now, Most of the studies related to tracking Maneuvering Targets are on fusing multiple Radars, or adding a supplementary sensor to INS and GPS. However, A study is required to change the degree of application in fusions since the system property and error property are different from sensors. In this paper, we perform the error analysis of the sensor properties by adding a ground radar to GPS and INS for improving the tracking performance by multi-sensor fusion, and suggest the tracking algorithm that improves the precision and stability by changing the sensor probability of each sensor according to the error. For evaluation, we extract the altitude values in a simulation for the trajectory of UAV and apply the suggested algorithm to carry out the performance analysis. In this study, we change the weight of the evaluated values according to the degree of error between the navigation information of each sensor to improve the precision of navigation information, and made it possible to have a strong tracking which is not affected by external purposed environmental change and disturbance.