• Journal of Cadastre & Land InformatiX
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• v.52 no.1
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• pp.81-93
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• 2022

Recently, there have been many studies on object recognition around the vehicle and recognition of traffic signs and traffic lights in autonomous driving. In particular, such the recognition of traffic lights is one of the core technologies in autonomous driving. Therefore, many studies for such the recognition of traffic lights have been performed, the studies based on various deep learning models have increased significantly in recent. In addition, as a high-quality AI training data set for voice, vision, and autonomous driving is released on AIHub, it makes it possible to develop a recognition model for traffic lights suitable for the domestic environment using the data set. In this study, we developed a recognition model for traffic lights that can be used in Korea using the AIHub's training data set. In particular, in order to improve the recognition performance, we used various models of YOLOv4 and YOLOv5, and performed our recognition experiments by defining various classes for the training data. In conclusion, we could see that YOLOv5 shows better performance in the recognition than YOLOv4 and could confirm the reason from the architecture comparison of the two models.

• Journal of Platform Technology
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• v.11 no.6
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• pp.106-115
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• 2023

In this paper, we propose a sensor data sharing system for the safe and smooth operation of autonomous vehicles on two-way single lanes in traffic-isolated areas and implement the core module, the situational awareness technology. Two-way single lanes pose challenges for autonomous vehicles, particularly when encountering parked vehicles or oncoming traffic, leading to reversing issues. We introduce a system using infrastructure cameras to detect vehicles' approach, enter, and leave on twoway single lanes in real-time, transmitting this information to autonomous vehicles via V2N communication, thereby expanding the sensing range of the autonomous vehicles. The core part of the proposed system is the situational awareness of the two-way single lane using infrastructure cameras. In this paper, we implement this using object detection and tracking technology. Finally, we validate the implemented situational awareness technology using data collected from actual two-way single lanes.

• Proceedings of the National Institute of Ecology of the Republic of Korea
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• v.3 no.2
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• pp.67-72
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• 2022

Ecological research relies on the interpretation of large amounts of visual data obtained from extensive wildlife surveys, but such large-scale image interpretation is costly and time-consuming. Using an artificial intelligence (AI) machine learning model, especially convolution neural networks (CNN), it is possible to streamline these manual tasks on image information and to protect wildlife and record and predict behavior. Ecological research using deep-learning-based object recognition technology includes various research purposes such as identifying, detecting, and identifying species of wild animals, and identification of the location of poachers in real-time. These advances in the application of AI technology can enable efficient management of endangered wildlife, animal detection in various environments, and real-time analysis of image information collected by unmanned aerial vehicles. Furthermore, the need for school education and social use on biodiversity and environmental issues using AI is raised. School education and citizen science related to ecological activities using AI technology can enhance environmental awareness, and strengthen more knowledge and problem-solving skills in science and research processes. Under these prospects, in this paper, we compare the results of our early 2013 study, which automatically identified African cichlid fish species using photographic data of them, with the results of reanalysis by CNN deep learning method. By using PyTorch and PyTorch Lightning frameworks, we achieve an accuracy of 82.54% and an F1-score of 0.77 with minimal programming and data preprocessing effort. This is a significant improvement over the previous our machine learning methods, which required heavy feature engineering costs and had 78% accuracy.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.101-103
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• 2022

CCTV is mainly used as a real-time detection system for critical facilities. In the case of CCTV, although the accuracy is high, the viewing angle is narrow, so it is used in combination with a sensor such as a radar. LiDAR is a technology that acquires distance information by detecting the time it takes to reflect off an object using a high-power pulsed laser. In the case of lidar, there is a problem in that the utilization is not high in terms of cost and technology due to the limitation of the number of simultaneous processing sensors in the server due to the data throughput. The detection method by the optical mesh sensor is also vulnerable to strong winds and extreme cold, and there is a problem of maintenance due to damage to animals. In this paper, by using the 1550nm wavelength band instead of the 905nm wavelength band used in the existing lidar sensor, the effect on the weather environment is strong and we propose to develop a system that can integrate and control multiple sensors.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2005.06a
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• pp.1819-1823
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• 2005

Incorrect information on public sites can cause serious problem. One of relevant countermeasures against this problem is to detect of buried underground utilities in real time. Although there have been several method to detect of buried underground utilities, such as investigating of gravity and elastic wave and electric field, they have not been so efficient tools. Because it is too expensive and difficult to use. In this paper, magnetic sensors which could provide an easier and more efficient method are used to detect of buried underground utilities. Also fluxgate method of self detection are used. Input signal is used $1\~10kHz$ frequency. Filtering and signal processing of output signal are used labview software. After experiment, detection system of buried underground utilities which used magnetic shows possibility of precise detecting of laying object based on theorectical analysis for electromagnetic field.

• Journal of the Korean Society of Marine Environment & Safety
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• v.10 no.2 s.21
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• pp.49-53
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• 2004

In this paper, we study a FDS(fishery detection system) for protection of an aquaculture farm. The FDS will identify a robbing vessel with real time and detect variance of the position of aquaculture farm. We also propose a F-AIS(Fishery- Automatic Identification System), which can detect the object approaching to aquaculture farm and distinguish fishing boats from thief vessel The F-AIS with low price and wideband responsibility will be adopted to the FDS.

• IEMEK Journal of Embedded Systems and Applications
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• v.10 no.1
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• pp.13-23
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• 2015

Recently, a few image-processing based mobile urine testers have actively been studied since the urine-analysis result can be available to the user in real time immediately after the test is done. However, the accuracy of test result can be severely degraded due to variable illumination environments and a variety of manners to capture the image with a camera embedded in the smartphone according to different users. This paper proposes the Check4Urine system, a novel smartphone-based portable urine-analysis tester and provides three techniques to improve such a performance degradation problem robust to various test environments and disturbances, which are the compensation algorithm to correct the varying illumination effect, an urine strip detection algorithm robust to edge loss of the object image, and the color decision algorithm based on the pre-processed reference table. Experimental results show that the proposed Check4Urine system increases the accuracy of urine-analysis by 20-50% at various test conditions, compared with the existing image-processing based mobile urine tester.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.4 no.4
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• pp.618-632
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• 2010

This paper presents a novel approach to control the augmented reality (AR) objects robustly in a marker-less AR system by fingertip tracking and hand pattern recognition. It is known that one of the promising ways to develop a marker-less AR system is using human's body such as hand or face for replacing traditional fiducial markers. This paper introduces a real-time method to manipulate the overlaid virtual objects dynamically in a marker-less AR system using both hands with a single camera. The left bare hand is considered as a virtual marker in the marker-less AR system and the right hand is used as a hand mouse. To build the marker-less system, we utilize a skin-color model for hand shape detection and curvature-based fingertip detection from an input video image. Using the detected fingertips the camera pose are estimated to overlay virtual objects on the hand coordinate system. In order to manipulate the virtual objects rendered on the marker-less AR system dynamically, a vision-based hand control interface, which exploits the fingertip tracking for the movement of the objects and pattern matching for the hand command initiation, is developed. From the experiments, we can prove that the proposed and developed system can control the objects dynamically in a convenient fashion.

• Korean Journal of Remote Sensing
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• v.35 no.2
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• pp.265-278
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• 2019

Visible Infrared Imaging Radiometer Suite (VIIRS) Day/Night Band (DNB) data help to enable rapid emergency responses through detection of the artificial and natural disasters occurring at night. The DNB data without correction of lunar irradiance effect distributed by Korea Ocean Science Center (KOSC) has advantage for rapid change detection because of direct receiving. In this study, radiance differences according to the phase of the moon was analyzed for urban and mountain areas in Korean Peninsula using the DNB data directly receiving to KOSC. Lunar irradiance correction algorithm was proposed for the change detection. Relative correction was performed by regression analysis between the selected pixels considering the land cover classification in the reference DNB image during the new moon and the input DNB image. As a result of daily difference image analysis, the brightness value change in urban area and mountain area was ${\pm}30$ radiance and below ${\pm}1$ radiance respectively. The object based change detection was performed after the extraction of the main object of interest based on the average image of time series data in order to reduce the matching and geometric error between DNB images. The changes in brightness occurring in mountainous areas were effectively detected after the calibration of lunar irradiance effect, and it showed that the developed technology could be used for real time change detection.

• Journal of the Korea Convergence Society
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• v.6 no.6
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• pp.1-8
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• 2015

In this paper, we proposed image enhancement method using sharpening filter for improving the accuracy of object detection using the existing Grabcut algorithm. GrabCut algorithm is the excellent performance extracting an object within a rectangular window range, but it has the drawback of the inferior performance in image with no clear distinction between background and objects. So, in this paper, reinforcing the brightness and clarity through histogram equalization, and tightening the border of the object using the sharpening filter look better than that extracted result of existing GrabCut algorithm in a similar image of the object and the background. Based on improved Grabcut algorithm, it is possible to obtain an improved result in the image processing convergence technique of character recognition, real-time object tracking and so on.