• International conference on construction engineering and project management
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• 2013.01a
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• pp.546-552
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• 2013

The future competitiveness of construction industry is dependent on K-12 students. However, unfavorable images of construction industry have negative influence on K-12 students' decision-making of their career. This negative image makes them not want to find out what actually happens in construction industry. Consequently, it is important to give K-12 students the opportunity to know what construction employees actually do in their job. Studies show that K-12 students who encounter the job early-on are more likely to choose it as their career. In this context, this paper proposes construction educational game in which it can serve as a medium for capturing K-12 students' interest in Construction Management (CM). Based on the literature reviews, challenges of construction educational game for K-12 students which are edutainment, hands-on experience and social interaction, are derived. To address these issues, conceptual model and scenario are designed. Based on designed scenario, prototype of Simulation based Construction Game in Virtual World (SCGVW) is developed in Second Life (SL) and applicability test to K-12 students are implemented. This paper concludes with a discussion of the lessons learned and the future development steps of the construction educational game for K-12 students.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.25 no.5
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• pp.427-436
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• 2007

Because the slope made up the construction of any other facilities, has many risks of a collapse, existing inspection methods to collect information for a construction site of slope bring up a long time of inspection period, cost and approach for a measuring instrument and it presents the critical point of collecting materials. For getting images to use zoom lens in any positions this study will use free zoomer constructed values of data classified by the focal length develop Image Loader system to make it load not only camera information but also camera test data values of the focal length took a photograph automatically if it measure to use a variety of cameras or other lens. Also, as it constructs three dimensions spatial image information from images of obtained objects this study presents effective basic materials of slope surveying and inspection and it shows exact surveying methods for dangerous slope not to access.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.12
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• pp.1790-1796
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• 2021

A large amount of image data sets are required for image deep learning, and there are many differences in the method of obtaining images and constructing image data sets depending on the type of object. In this paper, we presented a method of constructing an image data set for deep learning and analyzed the performance that varies depending on the object to be tracked. We took a video by rotating the object, and then created a data set by segmenting the video using the proposed data set construction method. As a result of performance analysis, detection rate was more than 95%, and detection rate of objects with little change in shape was higher performance. It is considered that it is effective to use the data set construction method presented in this paper for a situation in which it is difficult to obtain image data and to track an object with little change in shape within a video.

• Journal of Korean Society for Geospatial Information Science
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• v.8 no.2 s.16
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• pp.89-99
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• 2000

The Quality and Accuracy of digital image is important factor for decision of accuracy in digital photogrammetry because all the inside works in digital photogrammetry are based on digital image. But it is still difficult to ensure quality assurance and appication of data because there is no distinct criterion about quality and accuracy of digital image when the works in digital photogrammetry is accomplished. This study presents optimal resolution of aerial photo through error analysis of image coordinate using auto inner orientation in digital photograrnrnetry workstation. In second step, we are valified to optimum resolution of aerial photo image with orientation analysis. Finally, we are established to validity optimal resolution of aerial photo image with production of ortho image and mosaic image using optimal resolution aerial photo image.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2023.05a
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• pp.127-128
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• 2023

The construction industry is increasingly adopting vision AI technologies to improve efficiency and safety management. However, the complex and dynamic nature of construction sites can pose challenges to the accuracy of vision AI models trained on datasets that do not consider the background. This study investigates the effect of background on object recognition for vision AI in construction sites by constructing a learning dataset and a test dataset with varying backgrounds. Frame scaffolding was chosen as the object of recognition due to its wide use, potential safety hazards, and difficulty in recognition. The experimental results showed that considering the background during model training significantly improved the accuracy of object recognition.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2023.05a
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• pp.285-286
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• 2023

The visualization of information serves as a valuable tool for facilitating communication and exchange of opinions among stakeholders by conveying information in an intuitive and clear manner. As a preliminary study of visualization for construction field, this study proposed a model for generating three-dimensional floor layout using 360-degree panoramic cameras. The model integrates the layouts by calculating normal vectors of the plane which has openings, and applying translation and rotation matrices between the normal vectors. The results of this study can contribute to improving communication in construction sites by incorporating visualization, and further to the digital transformation of the construction industry.

• Journal of the Korean housing association
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• v.21 no.1
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• pp.67-77
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• 2010

Citizen participation in local redevelopment has recently been regarded as essential, since progress in democracy and diversified public interests have contributed to more importance being placed on citizen participation in the implementation of public policies. While the importance of resident participation has been increasingly emphasized in principle, in reality more effort is still required in its application. We need to develop practical strategies of collecting community opinion in order to reflect it in public policy, if we are to achieve a resident and citizen-centered society. The purpose of this study is to develop an image map construction tool that can be applied to the "Maul-Mandulgi" projects as a visualized method to facilitate the exchange of opinions and work toward agreements. The tool is intended to assist public discussion by visualizing policies and plans and reducing the possibility of misunderstanding, so that residents can properly respond to the plans. Second, this study will verify the effectiveness of the tool in the application to local community workshops. The main research method is participant observation method and field study. Major findings are as follows, First, every resident who had participated in previous workshops gathered together, used the tool and represented their opinions unusually more than once. Each resident tried to make sure that other participants appropriately understood his or her opinion. The workshop finished when all participants agreed and produced a consensus. The workshop took much less time, which is in stark contrast to previous workshops in which it took significantly more time to collect opinions. Second, it proved that residents in the redevelopment area can strike a broad agreement by themselves on a method and direction for residential improvement. In previous workshops, conflicts between residents developed over the choice between the two methods, of local improvement and total demolition prior to multi-housing construction. In this study, opinions of residents were not limited to the two methods by finding a winwin solution. Third, the use of the tool kit for image map became efficient for inactive residents to develop their own opinions in regard to the direction and orientations of the residential improvement process. In addition, for those who have either no or a slight understanding of the residential improvement projects, the tool can provide access to information and knowledge. This study concludes that the developed tool for imaging of the redevelopment projection like a design game, rather than using forms of text and speech, can be a useful tool in collecting opinions and forming an agreed opinion for forthcoming residential improvement plans.

• Journal of Korea Water Resources Association
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• v.42 no.5
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• pp.397-406
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• 2009

River is able to change by various environmental factors. In order to conduct restoration design of abandoned river channels, it is necessary to evaluate the river through the analysis of past and present river channels. River evaluation requires various data, such as geometry, hydraulic and hydrology, but there is a lot of difficulty to understand topographical information of river change on time and space due to a lack of past data by domestic conditions. This study analyzes the changes in past and present river channels and examines the applicability of river channel evaluation through image analysis using aerial photographs and 1918 year's map. Aerial photograph analysis was conducted by applying the image analysis method and GIS analysis method on Cheongmicheon. As a result of this analysis, we have quantitatively identified the form and size of abandoned channels, changes in the vertical-section and cross-section length of rivers, and micro-landform changes. More importantly, we verified that morphological changes in sandbars due to artificial straightening are important data in identifying the state of current river channels. In these results, although image analysis technique has limitations in two-dimensional information from aerial photographs, we were able to evaluate the changes in river channel morphology after artificial maintenance of the river.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.23 no.3
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• pp.199-206
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• 2023

This study utilizes deep learning algorithms to automatically classify construction waste into three categories: wood waste, plastic waste, and concrete waste. Two models, VGG-16 and ViT (Vision Transformer), which are convolutional neural network image classification algorithms and NLP-based models that sequence images, respectively, were compared for their performance in classifying construction waste. Image data for construction waste was collected by crawling images from search engines worldwide, and 3,000 images, with 1,000 images for each category, were obtained by excluding images that were difficult to distinguish with the naked eye or that were duplicated and would interfere with the experiment. In addition, to improve the accuracy of the models, data augmentation was performed during training with a total of 30,000 images. Despite the unstructured nature of the collected image data, the experimental results showed that VGG-16 achieved an accuracy of 91.5%, and ViT achieved an accuracy of 92.7%. This seems to suggest the possibility of practical application in actual construction waste data management work. If object detection techniques or semantic segmentation techniques are utilized based on this study, more precise classification will be possible even within a single image, resulting in more accurate waste classification

• KIPS Transactions on Software and Data Engineering
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• v.9 no.1
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• pp.33-38
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• 2020

This paper proposes a technique for counting construction materials by analyzing an image acquired by a Drone. The proposed technique use drone log which includes drone and camera information, RCNN for predicting construction material type, dummy area and Photogrammetry for counting the number of construction material. The existing research has large error ranges for predicting construction material detection and material dummy area, because of a lack of training data. To reduce the error ranges and improve prediction stability, this paper increases the training data with a method of data augmentation, but only uses rotated training data for data augmentation to prevent overfitting of the training model. For the quantity calculation, we use a drone log containing drones and camera information such as Yaw and FOV, RCNN model to find the pile of building materials in the image and to predict the type. And we synthesize all the information and apply it to the formula suggested in the paper to calculate the actual quantity of material pile. The superiority of the proposed method is demonstrated through experiments.