• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2001년도 ICCAS
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• pp.123.1-123
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• 2001

For Autonomous navigation of the mobile robots, the robots' capability to recognize 3D environment is necessary. In this paper, an on-line 3D map building method for autonomous mobile robots is proposed. To get range data on the environment, we use an sensor system which is composed of a structured light and a CCD camera based on optimal triangulation. The structured laser is projected as a horizontal strip on the scene. The sensor system can rotate $\pm$ $30{\Circ}$ with a goniometer. Scanning the system, we get the laser strip image for the environments and update planes composing the environment by some image processing steps. From the laser strip on the captured image, we find a center point of each column, and make line segments through blobbing these center poings. Then, the planes of the environments are updated. These steps are done on-line in scanning phase. With the proposed method, we can efficiently get a 3D map about the structured environment.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2001년도 ICCAS
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• pp.123.5-123
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• 2001

For autonomous navigation of the mobile robots, the robots' capability to recognize 3D environment is necessary. In this paper, an on-line 3D map building method for autonomous mobile robots is proposed. To get range data on the environment, we use a sensor system which is composed of a structured light and a CCD camera based on optimal triangulation. The structured laser is projected as a horizontal strip on the scene. The sensor system can rotate$\pm$30$^{\circ}$ with a goniometer. Scanning the system, we get the laser strip image for the environments and update planes composing the environment by some image processing steps. From the laser strip on the captured image, we find a center point of each column, and make line segments through blobbing these center points. Then, the planes of the environments are updated. These steps are done on-line in scanning phase. With the proposed method, we can efficiently get a 3D map about the structured environment.

• 조명전기설비학회논문지
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• 제14권2호
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• pp.18-25
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• 2000

도시 건물 외부에 조명을 비추기 시작한 이래로 건물경관조명의 중요성은 날로 증가되고 있는 추세이며 이에 따라 본 연구는 서울시의 경관조명을 대상으로 휘도측정, 문헌조사, 설문조사외 분석을 통하여 건물 경관조명 설계 기법을 제사하는데 있다. 본 연구의 결과로 건물상황조사, 건물 상황분석, 경관조명 가능성 선경, 시공 및 후속조치로 이루어전 경관조명 설계 기법 모델올 제시하였다.

• 한국건축시공학회:학술대회논문집
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• 한국건축시공학회 2015년도 춘계 학술논문 발표대회
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• pp.134-135
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• 2015

This study was conducted to present basic improvement measures to improve the negative construction industry image of construction workers in the future, by investigating the construction industry images that construction workers have, through previous studies and analyzing the factors influencing these images, based on the multiple regression model. But this study is limited to construction workers and failed to establish specific improvement measures. Therefore, continued research which remedies the limitation of this study, will performed in the future.

• 한국건축시공학회:학술대회논문집
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• 한국건축시공학회 2022년도 봄 학술논문 발표대회
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• pp.103-104
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• 2022

Cracks may occur in reinforced concrete (RC) structures due to various physical and chemical factors, and the growth of cracks causes deterioration of the structure's performance. It is important to prevent the expansion of cracks through periodic diagnosis of cracks in structures. In order to enable free crack exploration even in a narrow space, a construction robot using a Mecanum wheel that can move up, down, left and right and rotate in place was designed. High-quality crack images were periodically collected through the camera, and the image fragments stored during the exploration were combined into a single photo after the exploration was completed. The robot detected cracks with a width of 0.2 mm or more on the concrete probe surface with an accuracy of about 90% or more.

• 한국컴퓨터정보학회논문지
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• 제24권5호
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• pp.35-40
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• 2019

In this paper, we design and implement a partial denoising boundary matching system using indexing techniques. Converting boundary images to time-series makes it feasible to perform a fast search using indexes even on a very large image database. Thus, using this converting method we develop a client-server system based on the previous partial denoising research in the GUI(graphical user interface) environment. The client first converts a query image given by a user to a time-series and sends denoising parameters and the tolerance with this time-series to the server. The server identifies similar images from the index by evaluating a range query, which is constructed using inputs given from the client and sends the resulting images to the client. Experimental results show that our system provides many intuitive and accurate matching results.

• KIEAE Journal
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• 제7권4호
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• pp.31-38
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• 2007

Streetscape plays on important role in urban cities. Characteristics of streets is basically defined by the building facade, signs, plants and paving during the daytime. However at night, street receives a new appearance due to the shop light from the buildings, exterior lightings, signboards and street light, and it creates most of the image of the streets. This study aims to analyze the luminous environment and its subjective image of two different pedestrian's commercial streets. Insa-dong street and Myungdong street were chosen for the study. Horizontal illuminance and luminance on building surface, advertisement billboards and pedestrian road were measured. Thirty students were asked to rate the five scaled questionnaire on their subjective images of the streets. Statistical analysis including profile, correlation and T-test are conducted and some findings are discussed

• 한국측량학회지
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• 제30권6_1호
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• pp.583-590
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• 2012

본 연구에서는 고해상도 위성영상으로부터 세밀한 DEM 제작을 위해, 토지피복 특성에 따른 혼합 정합 방법을 제안하였다. 이를 위해, 고해상도 위성의 다밴드 영상에서 건물, 도로, 논 밭, 산림, 그림자 물로 이루어진 토지피복도를 제작하였으며, 흑백영상에서 에지와 포인터를 추출하였다. 정합수행 전에 5가지 분류항목을 자동 선별토록 하였으며, 이로부터 건물은 그리드와 에지 정합을, 도로는 그리드와 포인터 정합을, 논 밭과 산림지역은 그리드 정합만을 수행하고, 그림자 물은 정합대상에서 제외하였다. 그 결과, 기존 방법에 비해 지형 경계선을 보다 선명하게 표현하였으며, 건물이나 주거지 또한 더욱 잘 묘사하였다.

• 터널과지하공간
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• 제34권3호
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• pp.208-217
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• 2024

AI영상 기반 건설현장 안전관리 모니터링 시스템 개발 및 적용하는 추세에 다양한 환경변화에 따른 위험 객체 탐지 딥러닝 모델 개발에 많은 연구적 관심이 쏟아지고 있다. 여러 환경 변화요인 중 저조도 조건에서 객체 검출 모델의 정확도는 현저히 감소하며, 저조도 환경을 고려한 학습을 수행하더라도 일관적인 객체 탐지 정확도를 확보할 수 없다. 이에 따라 저조도 영상을 강화하는 영상 전처리 기술의 필요성이 대두된다. 따라서, 본 논문은 취득된 건설 현장 영상 데이터를 활용하여 다양한 딥러닝 기반 저조도 영상 강화 모델(GLADNet, KinD, LLFlow, Zero-DCE)을 학습하고, 모델별 저조도 영상 강화 성능을 비교 검증실험을 진행하였다. 저조도 강화된 영상을 시각적으로 검증하였고, 영상품질 평가 지수(PSNR, SSIM, Delta-E)를 도입하여 정량적으로 분석하였다. 실험 결과, GLADNet의 저조도 영상 강화 성능이 정량·정성적 평가에서 우수한 결과를 보여줬으며, 저조도 영상 강화 모델로 적합한 것으로 분석되었다. 향후 딥러닝 기반 객체 검출 모델에 저조도 영상 강화 기법이 전처리 단계로 적용한다면, 저조도 환경에서 일관된 객체 검출 성능을 확보할 것으로 예상된다.

• 한국건축시공학회:학술대회논문집
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• 한국건축시공학회 2023년도 가을학술발표대회논문집
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• pp.261-262
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• 2023

Drone is a suitable equipment for capturing images of cracks at construction sites based on its efficient mobility and high-resolution image acquisition capabilities. In this study, drone was used to acquire indoor construction sites framework images and deep learning technology was applied to detect cracks and measure width, and size. Finally, the usability of the process was verified based on the indoor crack monitoring system.