• 한국건축시공학회지
• /
• 제8권4호
• /
• pp.71-77
• /
• 2008

Earthwork is very equipment-intensive task and researches related to automated excavation have been conducted. There is an issue to secure the safety for an automated excavating system. Therefore, this paper focuses on how to improve safety for semi- or fully-automated backhoe excavation. The primary objective of this research is to develop the core technology for automated object detection in excavation work. In order to satisfy the research objective, a diverse sensing technologies are investigated and analysed in terms of functions, durability, and reliability. The authors developed detecting algorithm for the objects using laser sensor and verified its performance by several tests. The results of this study would be the basis for developing the automated object detection system.

• International Journal of Fuzzy Logic and Intelligent Systems
• /
• 제5권3호
• /
• pp.263-267
• /
• 2005

Building classifiers for financial real-world classification problems is often plagued by severely overlapping and highly skewed class distribution. New performance measures such as receiver operating characteristic (ROC) curve and area under ROC curve (AUC) have been recently introduced in evaluating and building classifiers for those kind of problems. They are, however, in-effective to evaluation of classifier's discrimination performance in a particular class of the classification problems that interests lie in only a local operating range of the classifier, In this paper, a new method is proposed that enables us to directly improve classifier's discrimination performance at a desired local operating range by defining and optimizing a partial area under ROC curve or domain-specific curve, which is difficult to achieve with conventional classification accuracy based learning methods. The effectiveness of the proposed approach is demonstrated in terms of fraud detection capability in a real-world fraud detection problem compared with the MSE-based approach.

• 대한원격탐사학회:학술대회논문집
• /
• 대한원격탐사학회 2003년도 Proceedings of ACRS 2003 ISRS
• /
• pp.923-926
• /
• 2003

In this paper we propose a scheme to generate DTM and detect buildings on DSM generated from LIDAR data. Two stages are performed. The first stage is to perform object segmentation by using two morphology operations namely, flattening and H-Dome transformation. After filtering out the object points above the ground, we used the non-object points to generate DTM. The second stage is to detect buildings from the objects by analyzing differential slopes. The test data is in raster form with 1m spacing around Hsin-Chu Scientific Area in Taiwan. The mean error is -0.16m and the RMSE is 0.45m for DTM generation. The successful rate for building detection is 87.7%.

• 한국건축시공학회:학술대회논문집
• /
• 한국건축시공학회 2022년도 봄 학술논문 발표대회
• /
• pp.73-74
• /
• 2022

Concrete structures occupy the largest proportion of modern infrastructure, and concrete structures often have cracking problems. Existing concrete crack diagnosis methods have limitations in crack evaluation because they rely on expert visual inspection. Therefore, in this study, we design a deep learning model that detects, visualizes, and outputs cracks on the surface of RC structures based on image data by using a CNN (Convolution Neural Networks) model that can process two- and three-dimensional data such as video and image data. do. An experimental study was conducted on an algorithm to automatically detect concrete cracks and visualize them using a CNN model. For the three deep learning models used for algorithm learning in this study, the concrete crack prediction accuracy satisfies 90%, and in particular, the 'InceptionV3'-based CNN model showed the highest accuracy. In the case of the crack detection visualization model, it showed high crack detection prediction accuracy of more than 95% on average for data with crack width of 0.2 mm or more.

• 한국건축시공학회:학술대회논문집
• /
• 한국건축시공학회 2022년도 가을 학술논문 발표대회
• /
• pp.245-246
• /
• 2022

Typically, the methods of crack detection on concrete structures include some problems, such as a low accuracy and expensive. To solve these problems, we proposed a neural network-based crack search method. The proposed algorithm goes through three convolutions and is classified into crack and non-crack through the softmax layer. As a result of the performance evaluation, cracks can be detected with an accuracy of 99.4 and 99.34 % at the training model and the validation model, respectively.

• 한국측량학회지
• /
• 제34권6호
• /
• pp.619-627
• /
• 2016

Cells of a PV (photovoltaic) module can suffer defects due to various causes resulting in a loss of power output. As a malfunctioning cell has a higher temperature than adjacent normal cells, it can be easily detected with a thermal infrared sensor. A conventional method of PV cell inspection is to use a hand-held infrared sensor for visual inspection. The main disadvantages of this method, when applied to a large-scale PV power plant, are that it is time-consuming and costly. This paper presents an algorithm for automatically detecting defective PV panels using images captured with a thermal imaging camera from an UAV (unmanned aerial vehicle). The proposed algorithm uses statistical analysis of thermal intensity (surface temperature) characteristics of each PV module to verify the mean intensity and standard deviation of each panel as parameters for fault diagnosis. One of the characteristics of thermal infrared imaging is that the larger the distance between sensor and target, the lower the measured temperature of the object. Consequently, a global detection rule using the mean intensity of all panels in the fault detection algorithm is not applicable. Therefore, a local detection rule was applied to automatically detect defective panels using the mean intensity and standard deviation range of each panel by array. The performance of the proposed algorithm was tested on three sample images; this verified a detection accuracy of defective panels of 97% or higher. In addition, as the proposed algorithm can adjust the range of threshold values for judging malfunction at the array level, the local detection rule is considered better suited for highly sensitive fault detection compared to a global detection rule. In this study, we used a panel area extraction method that we previously developed; fault detection accuracy would be improved if panel area extraction from images was more precise. Furthermore, the proposed algorithm contributes to the development of a maintenance and repair system for large-scale PV power plants, in combination with a geo-referencing algorithm for accurate determination of panel locations using sensor-based orientation parameters and photogrammetry from ground control points.

• 대한원격탐사학회:학술대회논문집
• /
• 대한원격탐사학회 2003년도 Proceedings of ACRS 2003 ISRS
• /
• pp.773-775
• /
• 2003

From the view point of data fusion, we integrate LIDAR data and digital aerial images to perform 3D building modeling in this study. The proposed scheme comprises two major parts: (1) building block extraction and (2) building model reconstruction. In the first step, height differences are analyzed to detect the above ground areas. Color analysis is then performed for the exclusion of tree areas. Potential building blocks are selected first followed by the refinement of building areas. In the second step, through edge detection and extracting the height information from LIDAR data, accurate 3D edges in object space is calculated. The accurate 3D edges are combined with the already developed SMS method for building modeling. LIDAR data acquired by Leica ALS 40 in Hsin-Chu Science-based Industrial Park of north Taiwan will be used in the test.

• 한국건축시공학회:학술대회논문집
• /
• 한국건축시공학회 2023년도 가을학술발표대회논문집
• /
• pp.255-256
• /
• 2023

The purpose of this study is to confirm the feasibility of exploring the location of reinforcing bars using the resistivity method in concrete structures.

• 한국재난정보학회 논문집
• /
• 제20권2호
• /
• pp.315-328
• /
• 2024

연구목적: 본 논문은 CCTV에서 확인하기 어려운 도심 생활안전 이상상황을 감지하기 위해 다종 복합 센서 정보를 활용한 서비스 구축방안을 제시하는데 목적이 있다. 연구방법:본 연구는 실제 테스트베드 데이터를 기반으로 서비스 시나리오를 선정하고, 주요 수요처인 지자체 스마트도시통합운영센터 운영자를 대상으로 서비스 중요도 분석을 수행하였다. 연구결과:서비스 시나리오는 크게 주야간 동적 객체 감지, 급격한 객체의 온도변화 감지, 시계열적 객체의 상대 온도변화 감지 유형으로 도출되었다. AHP 분석 결과, 사람, 차량 등 동적객체로 인한 보행, 모빌리티 충돌 위험상황 서비스와 즉각적인 대형 재난으로 이어지는 화재 전조현상 감지 서비스의 중요도가 높게 나타났다. 결론:본 연구는 테스트베드 실데이터 기반으로 지자체에서 활용 가능한 이상감지 서비스 구축방안을 제시한 의의가 있다. 이를 통해 지자체의 서비스 도입 의사결정을 지원하는 기초자료로 활용될 것으로 판단된다.

• 한국산학기술학회논문지
• /
• 제21권12호
• /
• pp.320-330
• /
• 2020

화재는 높은 비정형성으로 인해 딥러닝 모델을 이용한 영상인식 분야에서도 좋은 성능을 내기가 어려운 대상 중 하나이다. 특히 지하공동구 내 화재는 딥러닝 모델의 학습을 위한 화재 데이터 확보가 어렵고 열약한 영상 조건 및 화재로 오인할 수 있는 객체가 많아 화재 검출이 어렵고 성능이 낮다. 이러한 이유로 본 연구는 딥러닝 기반의 지하공동구 내 화재 탐지 모델을 제안하고, 제안된 모델의 성능을 평가하였다. 기존 합성곱 인공신경망에 GoogleNet의 Inception block과 ResNet의 skip connection을 조합하여 어두운 환경에서 발생되는 화재 탐지를 위한 모델 구조를 제안하였으며, 제안된 모델을 효과적으로 학습시키기 위한 방법도 함께 제시하였다. 제안된 방법의 효과를 평가하기 위해 학습 후 모델을 지하공동구 및 유사환경 조건의 화재 문제와 화재로 오인할 수 있는 객체를 포함한 이미지에 적용해 결과를 분석하였다. 또한 기존 딥러닝 기반 화재 탐지 모델의 정밀도, 검출률 지표와 비교함으로써 모델의 화재 탐지 성능을 정량적으로 평가하였다. 제안된 모델의 결과는 어두운 환경에서 발생되는 화재 문제에 대해 높은 정밀도와 검출률을 나타내었으며, 유사 화재 객체에 대해 낮은 오탐 및 미탐 성능을 가지고 있음을 보여주었다.