• Journal of Convergence for Information Technology
• /
• v.11 no.10
• /
• pp.45-52
• /
• 2021

In this paper, we proposed an Atrous Residual U-Net (AR-UNet) to improve the segmentation accuracy of semantic segmentation method based on U-Net. The U-Net is mainly used in fields such as medical image analysis, autonomous vehicles, and remote sensing images. The conventional U-Net lacks extracted features due to the small number of convolution layers in the encoder part. The extracted features are essential for classifying object categories, and if they are insufficient, it causes a problem of lowering the segmentation accuracy. Therefore, to improve this problem, we proposed the AR-UNet using residual learning and ASPP in the encoder. Residual learning improves feature extraction ability and is effective in preventing feature loss and vanishing gradient problems caused by continuous convolutions. In addition, ASPP enables additional feature extraction without reducing the resolution of the feature map. Experiments verified the effectiveness of the AR-UNet with Cityscapes dataset. The experimental results showed that the AR-UNet showed improved segmentation results compared to the conventional U-Net. In this way, AR-UNet can contribute to the advancement of many applications where accuracy is important.

• Journal of Korean Society of Forest Science
• /
• v.110 no.1
• /
• pp.72-81
• /
• 2021

We developed smartphone application and web application server to acquire and effectively manage tree measurement information. Smartphone applications can measure tree height, diameter at breast height (DBH), azimuth, altitude, slope, and positional coordinates using augmented reality (Google AR core) and motion sensors. The web application server effectively manages and stores measurement information. To evaluate the accuracy of information acquired using a smartphone, 90 Korean pine trees (Pinus koraiensis) were randomly selected from a natural mixed forest, with a total of 90 representative trees randomly collected from a natural mixed forest. Then, height and DBH were measured using a Haglof Vertex Laser Hypsometer and caliper. Comparisons of the results indicated significant results at the 95% level and a very high average correlation of 0.972 for both tree height and DBH. In terms of DBH, the average errors were 0.6745 cm and 1.0139 cm for artificial coniferous and natural mixed forests, respectively.

• Journal of the Korean Society for Aeronautical & Space Sciences
• /
• v.50 no.3
• /
• pp.157-164
• /
• 2022

In this paper, we describe how to implement the mountainous terrain, radar, and air defense network for UAV path planning in a 3-D environment, and perform path planning and re-planning using the PRM algorithm, a sampling-based path planning algorithm. In the case of the original PRM algorithm, the calculation to check whether there is an obstacle between the nodes is performed 1:1 between nodes and is performed continuously, so the amount of calculation is greatly affected by the number of nodes or the linked distance between nodes. To improve this part, the proposed LineGridMask method simplifies the method of checking whether obstacles exist, and reduces the calculation time of the path planning through parallelization. Finally, comparing performance with existing PRM algorithms confirmed that computational time was reduced by up to 88% in path planning and up to 94% in re-planning.

• The Journal of The Korea Institute of Intelligent Transport Systems
• /
• v.21 no.2
• /
• pp.17-32
• /
• 2022

This study explored an approach to estimate a flow-density diagram(FD) on a link in highway traffic environment by utilizing probe vehicles' time headway records. To study empirical flow-density diagram(EFD), the probe vehicles with vision sensors were recruited for collecting driving records for nine months and the vision sensor data pre-processing and GIS-based map matching were implemented. Then, we examined the new EFDs to evaluate validity with reference diagrams which is derived from loop detection traffic data. The probability distributions of time headway and distance headway as well as standard deviation of flow and density were utilized in examination. As a result, it turned out that the main factors for estimation errors are the limited number of probe vehicles and bias of flow status. We finally suggest a method to improve the accuracy of EFD model.

• Journal of Digital Convergence
• /
• v.19 no.5
• /
• pp.197-205
• /
• 2021

Recently, the retail industry has been increasingly demanding information technology convergence and utilization to respond to various external environmental threats such as COVID-19 and to be competitive using AI technologies, but there is a very lack of research and application services. This study is a CCTV video data-driven AI application case study, using CCTV image data collection in retail space, object detection and tracking AI model, time series database to store real-time tracked objects and tracking data, heatmap to analyze congestion and interest in retail space, social access zone.We present the orientation and verify its usability in the direction designed through practical implementation.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2023.05a
• /
• pp.2-2
• /
• 2023

기후변화가 가속화로 인해 수재해의 빈도와 강도 예측이 어려워짐에 따라 실시간 홍수 모니터링에 대한 수요가 증가하고 있다. 합성개구레이다는 광원과 날씨에 무관하게 촬영이 가능하여 수재해 발생시에도 영상을 확보할 수 있다. 합성개구레이다를 활용한 수체 탐지 알고리즘 개발이 활발히 연구되어 왔고, 딥러닝의 발달로 CNN을 활용하여 높은 정확도로 수체 탐지가 기능해졌다. 하지만, CNN 기반 수체 탐지 모델은 훈련시 높은 정량적 정확성 지표를 달성하여도 추론 후 정성적 평가시 경계와 소하천에 대한 탐지 정확성이 떨어진다. 홍수 모니터링에서 특히 중요한 정보인 경계와 좁은 하천에 대해서 정확성이 떨어짐에 따라 실생활 적용이 어렵다. 이에 경계를 강화한 적대적 학습 기반의 수체 탐지 모델을 개발하여 더 세밀하고 정확하게 탐지하고자 한다. 적대적 학습은 생성적 적대 신경망(GAN)의 두 개의 모델인 생성자와 판별자가 서로 관여하며 더 높은 정확도를 달성할 수 있도록 학습이다. 이러한 적대적 학습 개념을 수체 탐지 모델에 처음으로 도입하여, 생성자는 실제 라벨 데이터와 유사하게 수체 경계와 소하천까지 탐지하고자 학습한다. 반면 판별자는 경계 거리 변환 맵과 합성개구레이다 영상을 기반으로 라벨데이터와 수체 탐지 결과를 구분한다. 경계가 강화될 수 있도록, 면적과 경계를 모두 고려할 수 있는 손실함수 조합을 구성하였다. 제안 모델이 경계와 소하천을 정확히 탐지하는지 판단하기 위해, 정량적 지표로 F1-score를 사용하였으며, 육안 판독을 통해 정성적 평가도 진행하였다. 기존 U-Net 모델이 탐지하지 못하던 영역에 대해 제안한 경계 강화 적대적 수체 탐지 모델이 수체의 세밀한 부분까지 탐지할 수 있음을 증명하였다.

• KSCE Journal of Civil and Environmental Engineering Research
• /
• v.32 no.1D
• /
• pp.13-22
• /
• 2012

The 119 emergency rescue-aid service operated by Korean government is a very valuable in a society and its importance is growing in Korea as an aging society. Especially, the emergency vehicle's arrival time to accidents place is an important variable which affects initial emergency measure for patients and it depends on the road network attributes, such as emergency service station's location, accessibility to accidents place and so on. This study aims to analysis the emergency vehicles' arrival delay and the dispatch station in the viewpoint of efficiency utilizing the real rescue-aid activity data. We analyzed the dispatch distribution of the emergency rescue-aid service at first. And we analyzed high accident rate locations not involved in the fixed radius of rescue-aid service stations and display GIS map showing regions have been delayed. The input data of the road network speed is based on the KTDB (Korea Transportation Database) and historical rescue-aid data is from Gyeongsangbuk-do's fire service headquarters.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.20 no.12
• /
• pp.167-175
• /
• 2019

Recently, health examination centers have been changing from auxiliary medical facilities to key and independent medical facilities. However, it is not easy to improve medical facilities, including health examination centers, due to the variable characteristics of the relationship between humans and space. Therefore, this study was done to develop a pedestrian-based discrete event simulation analysis program to examine the problems and develop methods for improvement. The program was developed to analyze five evaluation indices and the density of examinees. The problems were derived by analyzing the required time, capacity, and queue size for each examination through simulations. We reduced the examination time and moving distance, increased the capacity, and distributed the queues by adjusting the medical services and relocating the examination rooms. The results were then quantitatively verified by simulations.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.25 no.5
• /
• pp.502-508
• /
• 2015

Stereo images have an advantage of calculating depth(distance) values which can not analyze from 2-D images. However, depth information obtained by stereo images has due to following reasons: it can be obtained by computation process; mismatching occurs when stereo matching is processing in occlusion which has an effect on accuracy of calculating depth information. Also, if global method is used for stereo matching, it needs a lot of computation. Therefore, this paper proposes the method obtaining disparity map which can reduce computation time and has higher accuracy than established method. Edge extraction which is image segmentation based on feature is used for improving accuracy and reducing computation time. Color K-Means method which is image segmentation based on color estimates correlation of objects in an image. And it extracts region of interest for applying Loopy Belief Propagation(LBP). For this, disparity map can be compensated by considering correlation of objects in the image. And it can reduce computation time because of calculating region of interest not all pixels. As a result, disparity map has more accurate and the proposed method reduces computation time.

• Journal of Korean Society for Geospatial Information Science
• /
• v.22 no.3
• /
• pp.57-69
• /
• 2014

As of 2013, Korean population density is 505 persons per $1km^2$ and is ranked 3rd place in the most densely populated countries exception of city-states. It shows clearly the population is concentrated in the city area. To fulfil this urban concentration population demand, the enlargement and complexation of buildings, subway and other underground spaces connection tendency has been intensified, and it is need to construct the indoor spatial information DB as well as the accurate indoor surveying DB to promote people's safety and social welfare. In this study, Sadang station and Incheon National Airport were aimed for the construction of Wi-Fi AP location DB and RadioMap DB by collecting the indoor AP raw datas by using mobile device and those collected results were ran through the process of verification, supplementation, and analyzation. To evaluate the performance of constructed DB, 10 points in Incheon Airport- 3rd flr in block A, and 9 points in Sadang station-B1 were selected and calculated the estimated points and ran evaluation experiment using survey positioning error, which is distance between real position and the estimated position. The result shows that Incheon international airport's average and standard deviation was separately 17.81m, 17.79m and Sadang station's average and standard deviation was separately 22.64m, 23.74m. In Sadang station's case, the areas near the exit has low performance of surveying position due to fewer visible AP points than other areas. As total datas were examined except those position, it was verified that the user's location was mapping close position in surveying positioning by using constructed DB. It means that constructed DB contains correct Wi-Fi AP locations and radio wave patterns in object region, so it is considered that the indoor spatial information service based on constructed DB would be available.