• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.10
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• pp.3858-3874
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• 2021

As an essential part of the urban transportation system, precise perception of the traffic flow parameters at the traffic signal intersection ensures traffic safety and fully improves the intersection's capacity. Traditional detection methods of road traffic flow parameter can be divided into the micro and the macro. The microscopic detection methods include geomagnetic induction coil technology, aerial detection technology based on the unmanned aerial vehicles (UAV) and camera video detection technology based on the fixed scene. The macroscopic detection methods include floating car data analysis technology. All the above methods have their advantages and disadvantages. Recently, indoor location methods based on wireless signals have attracted wide attention due to their applicability and low cost. This paper extends the wireless signal indoor location method to the outdoor intersection scene for traffic flow parameter estimation. In this paper, the detection scene is constructed at the intersection based on the received signal strength indication (RSSI) ranging technology extracted from the wireless signal. We extracted the RSSI data from the wireless signals sent to the road side unit (RSU) by the vehicle nodes, calibrated the RSSI ranging model, and finally obtained the traffic flow parameters of the intersection entrance road. We measured the average speed of traffic flow through multiple simulation experiments, the trajectory of traffic flow, and the spatiotemporal map at a single intersection inlet. Finally, we obtained the queue length of the inlet lane at the intersection. The simulation results of the experiment show that the RSSI ranging positioning method based on wireless signals can accurately estimate the traffic flow parameters at the intersection, which also provides a foundation for accurately estimating the traffic flow state in the future era of the Internet of Vehicles.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.16 no.6
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• pp.182-194
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• 2017

Most of the drone (Unmanned Aerial Vehicle) research in terms of traffic management involves detecting and tracking roads or vehicles. The purpose of analyzing image footage in the transportation sector is to overcome the limitations of the existing traffic data collection system (vehicle detectors, DSRC, etc.). With regards to this, drones are the good alternatives. However, due to limitation in their maximum flight time, they are appropriate to use as a complementary rather than replacing the existing collection system. Therefore, further research is needed for utilizing drones for transportation analysis purpose. Traffic problems often arise from one particular section or a point that expands to the whole road network and drones can be fully utilized to analyze these particular sections. Based on the study on the uses of traffic survey analysis, this study is conducted by extracting traffic flow parameters from video images(range 800~1000m) of highway unit segments that were taken by drones. In addition, video images were taken at a high altitude with the development of imaging technologies.

• International Journal of Aeronautical and Space Sciences
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• v.18 no.3
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• pp.545-554
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• 2017

Among various applications of the High Altitude Long Endurance (HALE) Unmanned Aerial Vehicle (UAV), this paper has a focus on the Global Positioning System (GPS) utilizing pseudolite and its improved performance, particularly during the multi-purpose missions. In a multi-purpose mission, the HALE UAV follows a specified flight trajectory for both navigation applications and missions. Some of the representative HALE missions are remote exploration, surveillance, reconnaissance, and communication relay. During these operations, the HALE UAV can also be an additional positioning signal source as it broadcast signals using pseudolite. The pseudolite signal can improve the availability, accuracy, and reliability of the GPS particularly in areas with poor signal reception, such as shadowed regions between tall buildings. The improvement in performance of navigation is validated through simulations of multi-purpose missions of the solar-powered HALE UAV in an urban canyon. The simulation includes UAV trajectory generation at stratosphere and uses actual geographical building data. The results indicate that the pseudolite-equipped HALE UAV has the potential to enhance the performance of the satellite navigation system in navigationally degraded regions even during multi-purpose operations.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.18 no.6
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• pp.155-163
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• 2019

This paper proposes a new methodology to recognize cracks on asphalt road surfaces using the image data obtained with drones. The target section was Yuseong-daero, the main highway of Daejeon. Furthermore, two object detection algorithms, such as Tiny-YOLO-V2 and Faster-RCNN, were used to recognize cracks on road surfaces, classify the crack types, and compare the experimental results. As a result, mean average precision of Faster-RCNN and Tiny-YOLO-V2 was 71% and 33%, respectively. The Faster-RCNN algorithm, 2Stage Detection, showed better performance in identifying and separating road surface cracks than the Yolo algorithm, 1Stage Detection. In the future, it will be possible to prepare a plan for building an infrastructure asset-management system using drones and AI crack detection systems. An efficient and economical road-maintenance decision-support system will be established and an operating environment will be produced.

• Korean Journal of Remote Sensing
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• v.33 no.6_2
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• pp.1139-1149
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• 2017

Measuring tree's volume is very important input data of various environmental analysis modeling However, It's difficult to use economical and equipment to measure a fragmented small green space in the city. In addition, Trees are sensitive to seasons, so we need new and easier equipment and quantification methods for measuring trees than lidar for high frequency monitoring. In particular, the tree's size in a city affect management costs, ecosystem services, safety, and so need to be managed and informed on the individual tree-based. In this study, we aim to acquire image data with UAV(Unmanned Aerial Vehicle), which can be operated at low cost and frequently, and quickly and easily quantify a single tree using SfM-MVS(Structure from Motion-Multi View Stereo), and we evaluate the impact of reducing number of images on the point density of point clouds generated from SfM-MVS and the quantification of single trees. Also, We used the Watertight model to estimate the volume of a single tree and to shape it into a 3D structure and compare it with the quantification results of 3 different type of 3D models. The results of the analysis show that UAV, SfM-MVS and solid model can quantify and shape a single tree with low cost and high time resolution easily. This study is only for a single tree, Therefore, in order to apply it to a larger scale, it is necessary to follow up research to develop it, such as convergence with various spatial information data, improvement of quantification technique and flight plan for enlarging green space.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.3
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• pp.155-160
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• 2018

The unmanned aerial vehicle (UAV) is used in various fields, such as land surveying, facility management, and disaster monitoring and restoration because it has low operational costs, fast data acquisition, and can generate a digital surface model (DSM). Recently, the UAV has been applied to process management in construction projects. Construction projects are widely distributed not only in urban areas but also in mountainous areas and rural areas where people are rarely in traffic or in vehicles. Projects range from a few hundred meters to several kilometers long. In order to perform a reconnaissance survey, a surveying method using a global positioning system (GPS) or a total station has mainly been used. However, these methods have a disadvantage in that a lot of time is required for data acquisition. This study's purpose is to evaluate the usability of a UAV DSM for surveying a construction area. Data was acquired using the UAV and a three-dimensional (3D) laser scanner, and the DSM of the construction site was created through data processing. The UAV DSM showed accuracy to within 30 cm based on the 3D laser scanner data, and a process comparison between the two work methods was able to present the usability of the UAV DSM in the field of construction surveying. Future utilization of the UAV DSM is expected to greatly improve the efficiency of work in construction projects.

• Korean Journal of Remote Sensing
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• v.40 no.1
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• pp.71-80
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• 2024

In this study, a proposed ensemble learning technique aims to enhance the semantic segmentation performance of images captured by Unmanned Aerial Vehicles (UAVs). With the increasing use of UAVs in fields such as urban planning, there has been active development of techniques utilizing deep learning segmentation methods for land cover segmentation. The study suggests a method that utilizes prominent segmentation models, namely U-Net, DeepLabV3, and Fully Convolutional Network (FCN), to improve segmentation prediction performance. The proposed approach integrates training loss, validation accuracy, and class score of the three segmentation models to enhance overall prediction performance. The method was applied and evaluated on a land cover segmentation problem involving seven classes: buildings,roads, parking lots, fields, trees, empty spaces, and areas with unspecified labels, using images captured by UAVs. The performance of the ensemble model was evaluated by mean Intersection over Union (mIoU), and the results of comparing the proposed ensemble model with the three existing segmentation methods showed that mIoU performance was improved. Consequently, the study confirms that the proposed technique can enhance the performance of semantic segmentation models.

• Korean Security Journal
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• no.58
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• pp.35-58
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• 2019

Drone was originally developed for air force aircraft or missile exercise shooting targets, and is being considered as the entire unmanned aircraft to the public. The core concept of a drone can be divided into 'unmanned' and 'aircraft'. However, there are many questions about whether the Fourth Industrial Revolution, expressed as a convergence scientific innovation, is appropriate at a time when smart cities are proposed as a concept of new urban spatial formation, and the role of self-driving vehicles, including drones, is being emphasized within the new urban integrated transport system. In this study, the concept of the existing drones was analyzed for the development process, definitions in each country's laws, and the results of the preceding research to present a concept suitable for future society and a unified term. It is not desirable to define a drone for the purpose of a country, an institution, or an operating entity, depending on the circumstances of the era. It is more reasonable to find the concept of a drone based on human life than in the traditional way, and more reasonable considering the development of the drones in the future. Subsequent studies should be more detailed, more data and research results analyzed, and discussed areas that were not covered in this study. Based on this, research should also be conducted on a variety of topics, including legislation, preparation of operational regulations, and related industrial processes and regulations.

• Journal of Advanced Navigation Technology
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• v.28 no.1
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• pp.68-76
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• 2024

With the recent rapid growth of the domestic and international unmanned aerial vehicle (UAV) market and the increasing importance of UAV operations in urban centers, such as UAMs, the safety management and regulatory framework for human life and property damage caused by UAV failures has been emphasized. In this study, we conducted a comparative analysis of risk-cost models that evaluate the risk of an operating area for safe UAM flight path planning, and identified the main limitations of each model to derive considerations for future model development. By providing a basic model for improving the safety of UAM operations, this study is expected to make an important contribution to technical improvements and policy decisions in the field of UAM flight path planning.

• Journal of the Society of Disaster Information
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• v.14 no.4
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• pp.501-509
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• 2018

Purpose: UAV-based photo measurements are being researched using UAVs in the space information field as they are not only cost-effective compared to conventional aerial imaging but also easy to obtain high-resolution data on desired time and location. In this study, the UAV-based high-resolution images were used to perform the land cover classification. Method: RGB cameras were used to obtain high-resolution images, and in addition, multi-distribution cameras were used to photograph the same regions in order to accurately classify the feeding areas. Finally, Land cover classification was carried out for a total of seven classes using created ortho image by RGB and multispectral camera, DSM(Digital Surface Model), NDVI(Normalized Difference Vegetation Index), GLCM(Gray-Level Co-occurrence Matrix) using RF (Random Forest), a representative supervisory classification system. Results: To assess the accuracy of the classification, an accuracy assessment based on the error matrix was conducted, and the accuracy assessment results were verified that the proposed method could effectively classify classes in the region by comparing with the supervisory results using RGB images only. Conclusion: In case of adding orthoimage, multispectral image, NDVI and GLCM proposed in this study, accuracy was higher than that of conventional orthoimage. Future research will attempt to improve classification accuracy through the development of additional input data.