• Journal of the Korean Society of Industry Convergence
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• v.23 no.5
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• pp.709-716
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• 2020

Recently, the drone's equipment development and software technology have dramatically improved. With such developments, the applicability is increasing in various fields that require rapid geospatial information, and in practice, regulations and systems have been established, and the fields in use are increasing. Also, in Korea, corporations and public institutions are actually using and researching drones in fields such as aircraft development, communication technology development, construction site use, and surveying. However, there are no fields where drones are actually used in road construction sites. Therefore, in this study, to utilize drones that have been actively used in the civil engineering and construction fields for road construction, we investigated the current status of the Korea Highway Corporation's field drone use research and classified the possibility of drone introduction by road construction. Finally, a method of using drones at road construction sites was proposed to prepare a method for using drones at road construction sites.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.39 no.1
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• pp.55-63
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• 2021

The roughness of the road is an important factor directly connected to the ride comfort, and is an evaluation item for functional evaluation and pavement quality management of the road. In this study, data on the road surface were acquired using the latest 3D geospatial information construction technology of ground LiDAR, drone photogrammetry, and drone LiDAR, and the accuracy and roughness of each method were analyzed. As a result of the accuracy evaluation, the average accuracy of terrestrial LiDAR were 0.039m, 0.042m, 0.039m RMSE in X, Y, Z direction, and drone photogrammetry and drone LiDAR represent 0.072~0.076m, 0.060~0.068m RMSE, respectively. In addition, for the roughness analysis, the longitudinal and lateral slopes of the target section were extracted from the 3D geospatial information constructed by each method, and the design values were compared. As a result of roughness analysis, the ground LiDAR showed the same slope as the design value, and the drone photogrammetry and drone LiDAR showed a slight difference from the design value. Research is needed to improve the accuracy of drone photogrammetry and drone LiDAR in measurement fields such as road roughness analysis. If the usability through improved accuracy can be presented in the future, the time required for acquisition can be greatly reduced by utilizing drone photogrammetry and drone LiDAR, so it will be possible to improve related work efficiency.

• KEPCO Journal on Electric Power and Energy
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• v.6 no.3
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• pp.271-278
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• 2020

KEPCO Research Institute developed a GIS-based autopilot drone inspection system for electric power facilities and since its pilot application in 2017, it has been successfully used to detect defects in power transmission lines. This paper presents how to operate this system in the field of power line inspection. Power transmission lines are located in a wide variety of environments such as plains, mountains, river crossings, sea crossings, and industrial areas. Among these, some transmission lines are difficult for human workers to access because of their geographies and some should be checked more often due to their severe contamination. Considering these field conditions, we classified drone operation in two categories to increase its effectiveness and efficiency - patrol surveillance and detailed inspection tasks. This paper describes the detailed procedures of the two tasks above and their field application experiences. In addition, this paper newly proposes how to construct public drone roads by using the information of KEPCO's power transmission lines.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.38 no.2
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• pp.131-140
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• 2020

Recently the importance of BIM (Building Information Modeling) that enables 3D location-based design and construction work is being highlighted around the world. In Korea, the road map has been established to settle the design based on BIM using drone survey results by 2025. As the first step, BIM would be applied to road construction projects worth more than 50 billion Korean Won from 2020. On the other hand, drone survey regulation has been enacted and the data for drone survey cost were also included on Standard of construction estimate in 2020. However, more careful improvement is required to reflect drone survey results in BIM design and construction. Currently, Engineering instructions and Standard of construction estimate specifies that earthwork volume must be calculated by cross section method only. So it is required to add the method of DEM (Digital Elevation Model) based volume calculation on these regulations to realize BIM application. In order for that, this study verified the method of DEM based earthwork volume calculation. To get an accurate DEM for accurate volume computation, drone survey was carried out according to the drone survey regulation and then could get an accurate DEM data which have errors less than 3cm in X, Y and 6.8cm in H. As each DEM cell has 3D coordinate component, the volume of each cell can be calculated by obtaining the height of area of the cell then total volume is calculated by multiplying total number of cells by volume of each cell for the construction area. Verification for the new calculation method compare with existing method was carried out. The difference between DEM based volume by drone survey and cross section based volume by traditional survey was less than 1.33% and it can be seen that new DEM method will be able to be applied to BIM design and construction instead of cross section method.

• International Journal of Highway Engineering
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• v.20 no.4
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• pp.73-84
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• 2018

PURPOSES : Most of the traffic surveys are carried out by an inspection method by the manpower. In some cases, the video equipment is used only in some regions when the traffic volume is surveyed. In this case, there is environmental restriction that the road equipment to fix the video equipment should exist. Also, in areas where information such as digital maps and satellite photographs is old or not provided, they are forced to rely on manpower research, but it is difficult to put huge amounts of time and money into the research in places where labor supply and demand is difficult. This difficulty is particularly pronounced in overseas business. METHODS : The purpose of this study is to improve the efficiency of business by using the drones in the traffic investigation through analysis of the cost reduction effect between the existing method and the proposed method in the overseas business traffic survey. The scope of the research is limited to the scope of research, and based on the case of overseas projects, the method of using drone for each research sector is suggested. RESULTS : In the traffic condition survey section, we propose the use of drone for traffic survey and queue length survey, and it is confirmed that there is a cost saving effect of 16% ~ 27% compared with the existing method. In the road condition survey, we propose the use of drones for road surface linear survey, geometry survey, and obstacle survey, and it was confirmed that the cost reduction effect is 39% ~ 93% compared to the existing method. CONCLUSIONS : In addition to overseas business, it is expected that it will have a positive effect on research time and cost reduction by using drone in traffic survey in domestic area where labor supply is not easy or basic data is insufficient.

• Electronics and Telecommunications Trends
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• v.33 no.4
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• pp.70-80
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• 2018

Drones are now widely used in civilian applications such as filming, leisure, agricultural control, monitoring, and the generation of 3D-spatial information, deviating only from military drones. In the field of logistics, prototypes are emerging in the area of logistics transportation, and to develop a future transportation service under the name of a drone tax, each country is introducing its first flight results using its own unique drones. In this paper, we review the domestic and overseas trends of drone delivery service technology, which requires various capabilities such as automatic flight, and review the related core technologies. We then propose the flight capability and road map of a drone delivery service according to the detailed conditions such as the flight area, visibility, and flight method. Additionally, in connection with the postal processing of the Korea Post Office, which would be a main demand for this type of service, we describe a method for realizing a drone delivery service based on the structure, scenario, and deployment of the drone delivery system.

• Advances in aircraft and spacecraft science
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• v.10 no.6
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• pp.495-519
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• 2023

The drone serves the customers not served by vans. At the same time, considering the safety, policy and terrain as well as the need to replace the battery, the drone needs to be transported by truck to the identified station along with the parcel. From each such station, the drone serves a subset of customers according to a direct assignment pattern, i.e., every time the drone is launched, it serves one demand node and returns to the station to collect another parcel. Similarly, the truck is used to transport the drone and cargo between stations. This is somewhat different from the research of other scholars. In terms of the joint distribution of the drone and road vehicle, most scholars will choose the combination of two transportation tools, while we use three. The drone and vans are responsible for distribution services, and the trucks are responsible for transporting the goods and drone to the station. The goal is to optimize the total delivery cost which includes the transportation costs for the vans and the delivery cost for the drone. A fixed cost is also considered for each drone parking site corresponding to the cost of positioning the drone and using the drone station. A discrete optimization model is presented for the problem in addition to a two-phase heuristic algorithm. The results of a series of computational tests performed to assess the applicability of the model and the efficiency of the heuristic are reported. The results obtained show that nearly 10% of the cost can be saved by combining the traditional delivery mode with the use of a drone and drone stations.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.37 no.4
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• pp.243-251
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• 2019

The road sign location information installed on national roads is continuously updated using MMS (Mobile Mapping System) technology. It is possible to map accurate road facilities by MMS, but the equipment is very expensive and requires specialized technology. Also, the accuracy of the position of the object greatly depends on the GPS (Global Positioning System) accuracy. In the case of road facility mapping, the advantage of drone is more remarkable than that of field survey or conventional aerial photogrammetry. In particular, it is more efficient than field surveying and it is possible to acquire high resolution images with low budget compared to conventional aerial photogrammetry. In this study, the accuracy of the location information measured by the existing MMS is compared with the GPS survey result and the accuracy analysis is performed by the drone aerial photogrammetry. In order to confirm the space accuracy that can be obtained when conducting drone aerial photogrammetry, the accuracy of the change in the number of ground control points and the degree of overlap was evaluated. As a result of the experiment, it was possible to obtain sufficient accuracy with two ground control points distributed at both ends of the road and 60% overlap.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.41 no.4
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• pp.461-468
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• 2021

The role of drones is increasing day by day in smart construction that manages construction sites with 3D data in every life cycles. This is because both the digital surface model (DSM) and the orthoimage obtained for the construction site through the drone are made of point cloud data. This study aims to develop standard work types for drone use in order to systematically utilize drones in expressway construction sites. For the study, two expressway construction sites in Korea were set as test beds, and construction types applicable to drones were derived and verified through a pilot project. As a result of the study, three construction work types were developed for road planning, road design and maintenance, respectively, and in road construction, twenty-one detailed construction types were developed for five construction work types. It is expected that drones can be used more systematically not only at expressway construction sites, but also at other road construction sites by utilizing the "standard work type at expressway construction site for drone use" developed in this study.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.40 no.3
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• pp.147-157
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• 2022

Recently, many studies have been conducted to analyze traffic or object recognition that classifies vehicles through artificial intelligence-based prediction models using CCTV (Closed Circuit TeleVision)or drone images. In order to develop an object recognition deep learning model for accurate traffic estimation, systematic data construction is required, and related standardized guidelines are insufficient. In this study, previous studies were analyzed to derive guidelines for establishing artificial intelligence-based training data for traffic estimation using drone images, and business reports or training data for artificial intelligence and quality management guidelines were referenced. The guidelines for data construction are divided into data acquisition, preprocessing, and validation, and guidelines for notice and evaluation index for each item are presented. The guidelines for data construction aims to provide assistance in the development of a robust and generalized artificial intelligence model in analyzing the estimation of road traffic based on drone image artificial intelligence.