• The Journal of Korean Institute of Information Technology
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• v.16 no.12
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• pp.13-24
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• 2018

Recently, light pollution has become a serious environment issue caused by excessive uses of artificial light. Central and local governments have made efforts to manage light pollution and mitigate light pollution damages. Developing methods to diagnose light pollution is critical to effectively monitor light pollution conditions in Seoul. This study develops a methodology to create a map that presents the status of light pollution in Seoul, using International Space Station(ISS) night-time images. Through the map, we evaluated the areas that show high levels of light intensity and found out local characteristics of light intensity; Commercial area, office building concentrated area, and large sports facilities. The result of study provides basic understanding to present a new way for monitoring light pollution in urban sites.

• Journal of the Society of Disaster Information
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• v.19 no.3
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• pp.689-697
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• 2023

Purpose: The purpose of this study is to automatically extract information on underground facilities using a general-purpose smartphone in the process of applying the mini-trenching method. Method: Data sets for image learning were collected under various conditions such as day and night, height, and angle, and the object detection algorithm used the R-CNN algorithm. Result: As a result of the study, F1-Score was applied as a performance evaluation index that can consider the average of accurate predictions and reproduction rates at the same time, and F1-Score was 0.76. Conclusion: The results of this study showed that it was possible to extract information on underground buried materials based on smartphones, but it is necessary to improve the precision and accuracy of the algorithm through additional securing of learning data and on-site demonstration.

• Journal of the Korea Society of Computer and Information
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• v.26 no.1
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• pp.45-55
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• 2021

This paper has proposed an algorithm that detecting for dense small vehicle in large image efficiently. It is consisted of two Ensemble Deep-Learning Network algorithms based on Coarse to Fine method. The system can detect vehicle exactly on selected sub image. In the Coarse step, it can make Voting Space using the result of various Deep-Learning Network individually. To select sub-region, it makes Voting Map by to combine each Voting Space. In the Fine step, the sub-region selected in the Coarse step is transferred to final Deep-Learning Network. The sub-region can be defined by using dynamic windows. In this paper, pre-defined mapping table has used to define dynamic windows for perspective road image. Identity judgment of vehicle moving on each sub-region is determined by closest center point of bottom of the detected vehicle's box information. And it is tracked by vehicle's box information on the continuous images. The proposed algorithm has evaluated for performance of detection and cost in real time using day and night images captured by CCTV on the road.

• Korean Institute of Interior Design Journal
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• v.22 no.4
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• pp.86-93
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• 2013

Illumination in church building has played an important role in making workship space more suitable for divine service. As time has changed, however, the worship space of Protestantism is no longer somber and reverent, but changing into practical and multipurpose space with bright and colorful image. This study investigates intensity of illumination of worship space during day and night respectively and figures out the change of psychological change of congregation in accordance with brightness and color temperature in worship space in order to find out preference and look into the usage and change of illumination in recent Protestantism in compliance with the needs of the time and influence. Then, result value is to be compared to Korean Standard intensity of illumination. Also, the correlation between intensity of illumination and color temperature by Kruithof's curve theory is used to identify the comfortableness of the worship space during day and nigh. And it is a common task of space designers and pastors to map out the worship space in terms of illumination suitable for its own purpose with even more modern and Korean sensibility and to create spiritual space for worshippers by collecting opinions from congregation, the main user of the space. With appropriate usage of illumination in Protestantism worship space, the atmosphere and purpose of worship can be enhanced.

• Journal of the Korea Institute of Military Science and Technology
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• v.20 no.2
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• pp.181-186
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• 2017

Synthetic aperture radar(SAR) has been widely used for reconnaissance. It provides high-resolution, day-and-night and weather-independent images for a multitude of applications. Because SAR coherently combines many viewing angles to effectively create a large aperture(narrow beam) radar, the test-bed should be capable of moving straightly SAR sensor for the integration angle to meet resolution. This paper describes the test-bed developed to test and evaluate the SAR performance. It forms high-quality images in real time and saves the raw data for the purpose of post processing on the ground.

• The Journal of the Korea institute of electronic communication sciences
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• v.19 no.3
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• pp.563-570
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• 2024

This paper proposes a method to recognize and track drivable lane areas to assist the driver. The main topic is designing a deep-based network that predicts drivable road areas using computer vision and deep learning technology based on images acquired in real time through a camera installed in the center of the windshield inside the vehicle. This study aims to develop a new model trained with data directly obtained from cameras using the YOLO algorithm. It is expected to play a role in assisting the driver's driving by visualizing the exact location of the vehicle on the actual road consistent with the actual image and displaying and tracking the drivable lane area. As a result of the experiment, it was possible to track the drivable road area in most cases, but in bad weather such as heavy rain at night, there were cases where lanes were not accurately recognized, so improvement in model performance is needed to solve this problem.

• Journal of the Korean Association of Geographic Information Studies
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• v.15 no.2
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• pp.113-125
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• 2012

This research is aimed at evaluating the land surface characterization of KOMPSAT-3A middle infrared (MIR) data. Airborne Hyperspectral Scanner (AHS) data, which has MIR bands with high spatial resolution, were used to assess land surface temperature (LST) retrieval and classification accuracy of MIR bands. Firstly, LST values for daytime and nighttime, which were calculated with AHS thermal infrared (TIR) bands, were compared to digital number of AHS MIR bands. The determination coefficient of AHS band 68 (center wavelength $4.64{\mu}m$) was over 0.74, and was higher than other MIR bands. Secondly, The land cover maps were generated by unsupervised classification methods using the AHS MIR bands. Each class of land cover maps for daytime, such as water, trees, green grass, roads, roofs, was distinguished well. But some classes of land cover maps for nighttime, such as trees versus green grass, roads versus roofs, were not separated. The image classification using the difference images between daytime AHS MIR bands and nighttime AHS MIR bands were conducted to enhance the discrimination ability of land surface for AHS MIR imagery. The classification accuracy of the land cover map for zone 1 and zone 2 was 67.5%, 64.3%, respectively. It was improved by 10% compared to land cover map of daytime AHS MIR bands and night AHS MIR bands. Consequently, new algorithm based on land surface characteristics is required for temperature retrieval of high resolution MIR imagery, and the difference images between daytime and nighttime was considered to enhance the ability of land surface characterization using high resolution MIR data.

• The KIPS Transactions:PartB
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• v.18B no.3
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• pp.117-130
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• 2011

The purpose of this study is to produce a better way to detect crossroad accidents, which involves an efficient method to produce background images in consideration of object movement and preserve/demonstrate the candidate accident region. One of the prior studies proposed an employment of traffic signal interval within crossroad to detect accidents on crossroad, but it may cause a failure to detect unwanted accidents if any object is covered on an accident site. This study adopted inverse perspective mapping to control the scale of object, and proposed different ways such as producing robust background images enough to resist surrounding noise, generating candidate accident regions through information on object movement, and by using edge information to preserve and delete the candidate accident region. In order to measure the performance of proposed algorithm, a variety of traffic images were saved and used for experiment (e.g. recorded images on rush hours via DVR installed on crossroad, different accident images recorded in day and night rainy days, and recorded images including surrounding noise of lighting and shades). As a result, it was found that there were all 20 experiment cases of accident detected and actual effective rate of accident detection amounted to 76.9% on average. In addition, the image processing rate ranged from 10~14 frame/sec depending on the area of detection region. Thus, it is concluded that there will be no problem in real-time image processing.

• Journal of Aerospace System Engineering
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• v.12 no.1
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• pp.10-16
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• 2018

In 2018 and 2019, GEO-KOMPSAT-2A and GEO-KOMPSAT-2B will be launched in order to succeed the COMS mission. The two satellites will be collocated in $128.25{\pm}0.05$ degrees East. For precise ranging and orbit determination, the GEO-KOMPSAT-2B will be equipped with LRA (Laser Retroreflector Assembly) and SLR (Satellite Laser Ranging) systems will be utilized. This systems are located in Geochang. In this case, the laser beam emitted from the SLR station can cause problems in terms of safety of optical payloads and image quality. As a solution of this possibility, the laser ranging will be done during the night time when the shutters of the optical payloads remain closed. Still, the optical payload of the GEO-KOMPSAT-2A is not safe from the laser beam because its optical payload shall continue its mission for 24 hours a day. In order to handle this problem, the laser ranging shall be limited to time slots when the angular distance between two satellites observed from the Geochang SLR station is large enough. In this paper, through orbit simulations, the characteristics of variation of the angular distance between the two satellites is analyzed to figure out the time slots when laser ranging is allowed.

• Journal of Internet Computing and Services
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• v.16 no.2
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• pp.49-55
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• 2015

According to the statistics of traffic accidents over recent 5 years, traffic accidents during the night times happened more than the day times. There are various causes to occur traffic accidents and the one of the major causes is inappropriate or missing street lights that make driver's sight confused and causes the traffic accidents. In this paper, with smartphones, we designed and implemented a lane luminance measurement application which stores the information of driver's location, driving, and lane luminance into database in real time to figure out the inappropriate street light facilities and the area that does not have any street lights. This application is implemented under Native C/C++ environment using android NDK and it improves the operation speed than code written in Java or other languages. To measure the luminance of road, the input image with RGB color space is converted to image with YCbCr color space and Y value returns the luminance of road. The application detects the road lane and calculates the road lane luminance into the database sever. Also this application receives the road video image using smart phone's camera and improves the computational cost by allocating the ROI(Region of interest) of input images. The ROI of image is converted to Grayscale image and then applied the canny edge detector to extract the outline of lanes. After that, we applied hough line transform method to achieve the candidated lane group. The both sides of lane is selected by lane detection algorithm that utilizes the gradient of candidated lanes. When the both lanes of road are detected, we set up a triangle area with a height 20 pixels down from intersection of lanes and the luminance of road is estimated from this triangle area. Y value is calculated from the extracted each R, G, B value of pixels in the triangle. The average Y value of pixels is ranged between from 0 to 100 value to inform a luminance of road and each pixel values are represented with color between black and green. We store car location using smartphone's GPS sensor into the database server after analyzing the road lane video image with luminance of road about 60 meters ahead by wireless communication every 10 minutes. We expect that those collected road luminance information can warn drivers about safe driving or effectively improve the renovation plans of road luminance management.