• IEMEK Journal of Embedded Systems and Applications
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• v.17 no.5
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• pp.273-280
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• 2022

Along with the advancement of deep learning technology, securing high-quality dataset for verification of developed technology is emerging as an important issue, and developing robust deep learning models to the domestic road environment is focused by many research groups. Especially, unlike expressways and automobile-only roads, in the complex city driving environment, various dynamic objects such as motorbikes, electric kickboards, large buses/truck, freight cars, pedestrians, and traffic lights are mixed in city road. In this paper, we built our dataset through multi camera-based processing (collection, refinement, and annotation) including the various objects in the city road and estimated quality and validity of our dataset by using YOLO-based model in object detection. Then, quantitative evaluation of our dataset is performed by comparing with the public dataset and qualitative evaluation of it is performed by comparing with experiment results using open platform. We generated our 2D dataset based on annotation rules of KITTI/COCO dataset, and compared the performance with the public dataset using the evaluation rules of KITTI/COCO dataset. As a result of comparison with public dataset, our dataset shows about 3 to 53% higher performance and thus the effectiveness of our dataset was validated.

• Journal of the Semiconductor & Display Technology
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• v.23 no.3
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• pp.135-139
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• 2024

Public datasets, which are freely available and often labeled, play a crucial role in training object detection models in computer vision. While public datasets are effective for developing general object detection models, they may not be ideal for specialized tasks. For specific object detection needs, it is more beneficial to create and use a dataset tailored to the target object. This paper proposes a method for extracting a target-specific dataset from public datasets to develop object detection models with superior performance for the target object. This approach not only improves detection accuracy, but also reduces training data requirements and complexity. We evaluate the performance of the proposed method using the latest object detection model YOLOv10.

• The Korean Journal of Archival Studies
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• no.71
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• pp.237-272
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• 2022

In 2019, the pilot project to establish a record management system for administrative information datasets started in earnest under the leadership of the National Archives. Based on the results of the three-year project by 2021, the improved administrative information dataset management plan will be reflected in public records-related laws and guidelines. Through this, the administrative information dataset becomes the target of full-scale public record management. Although public records have been converted to electronic documents and even the datasets of administrative information systems have been included in full-scale public records management, research on the quality requirements of data itself as raw data constituting records is still lacking. If data quality is not guaranteed, all four properties of records will be threatened in the dataset, which is a structure of data and an aggregate of records. Moreover, if the reliability of the quality of the data of the administrative information system built by reflecting the various needs of the working departments of the institution without considering the standards of the standard records management system is insufficient, the reliability of the public records itself can not be secured. This study is based on the administrative information dataset management plan presented in the "Administrative Information Dataset Recorded Information Service and Utilization Model Study" conducted by the National Archives of Korea in 2021. A study was conducted. By referring to various data, especially public data-related policies and guides, which are being promoted across the government, we would like to derive quality evaluation requirements in terms of records management and present specific indicators. Through this, it is expected that it will be helpful for record management of administrative information dataset which will be in full swing in the future.

• Journal of the Korean Society for information Management
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• v.38 no.3
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• pp.263-285
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• 2021

It is necessary to recognize the urgency and importance of administrative information dataset management and study effective management measures and specific procedures applicable in practice. Particularly, identify dataset records and developing records schedule for records management needs to be presented in detail and specific. This study designed and verified an identification method and appraisal procedure of dataset records in public administrative information systems dataset operating in public institutions. In addition, this study presented the role of the participants including the records center in the appraisal process. Through this, useful implications are derived for the development of specific and practical processes and tools for dataset management in the records center.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.2
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• pp.365-380
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• 2022

The target detection algorithm based on supervised learning is the current mainstream algorithm for target detection. A high-quality dataset is the prerequisite for the target detection algorithm to obtain good detection performance. The larger the number and quality of the dataset, the stronger the generalization ability of the model, that is, the dataset determines the upper limit of the model learning. The convolutional neural network optimizes the network parameters in a strong supervision method. The error is calculated by comparing the predicted frame with the manually labeled real frame, and then the error is passed into the network for continuous optimization. Strongly supervised learning mainly relies on a large number of images as models for continuous learning, so the number and quality of images directly affect the results of learning. This paper proposes a dataset STAR-24K (meaning a dataset for Space TArget Recognition with more than 24,000 images) for detecting common targets in space. Since there is currently no publicly available dataset for space target detection, we extracted some pictures from a series of channels such as pictures and videos released by the official websites of NASA (National Aeronautics and Space Administration) and ESA (The European Space Agency) and expanded them to 24,451 pictures. We evaluate popular object detection algorithms to build a benchmark. Our STAR-24K dataset is publicly available at https://github.com/Zzz-zcy/STAR-24K.

• The Korean Journal of Archival Studies
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• no.25
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• pp.91-129
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• 2010

As it grows more heavily reliant on information system, researchers seek for various ways to manage and utilize of dataset records which is accumulated in public information system. It might be needed to adjust date and enhance the quality of public administrative dataset records during transferring to archive system or sharing server. The purpose of this paper is presenting data adjustment and quality enhancement methods for public administrative dataset records, and it refers to ETT procedure and method of construction of datawarehouses. It suggests seven typical examples and processing method of data adjustment and quality enhancement, which are (1) verification of quantity and data domain (2) code conversion for a consistent code value (3) making component with combinded information (4) making a decision of precision of date data (5) standardization of data (6) comment information about code value (7) capturing of metadata. It should be reviewed during dataset record transfer. This paper made Data adjustment and quality enhancement requirements for dataset record transfer, and it could be used as data quality requirement of administrative information system which produces dataset.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.43 no.1
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• pp.99-107
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• 2023

Recently, the rate of death and safety accidents at construction sites is the highest among all kinds of industries. In order to apply artificial intelligence technology to construction sites, it is essential to secure a dataset which can be used as a basic training data. In this paper, a number of image data were collected through actual construction site, for which major construction equipment objects mainly operated in civil engineering sites were defined. The optimal training dataset construction was completed by annotation process of about 90,000 image dataset. Reliability of the dataset was verified with the mAP of over 90 % in use of YOLO, a representative model in the field of object detection. The construction equipment training dataset built in this study has been released which is currently available on the public data portal of the Ministry of Public Administration and Security. This dataset is expected to be freely used for any application of object detection technology on construction sites especially in the field of construction safety in the future.

• Journal of Korean Society of Archives and Records Management
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• v.21 no.1
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• pp.75-95
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• 2021

In this study, we focused on creating plans to manage the administrative information dataset of public records in closed universities. In particular, according to various reference materials and internal materials of the institution, we studied the theoretical discussion about the dataset and figured out the management status of the closed university's dataset. Therefore, as a measure for the data management of the Comprehensive Information Management System, recording targets are selected, retention periods are determined, administrative information dataset management standards are prepared, administrative information dataset evaluation and deletion are implemented, and comprehensive management systems of closed universities are established.

• Journal of Korean Society of Archives and Records Management
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• v.22 no.2
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• pp.27-49
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• 2022

According to the amendment of the Enforcement Decree of the Public Records Management Act in 2020, an administrative information dataset record management plan will be enacted, and the National Archives of Korea plans to establish an integrated administrative information dataset management system to support it. However, there is no specific service plan that considers the characteristics of the datasets and the Management Reference Table. Therefore, this paper compared and analyzed the current status of dataset services at 14 domestic and foreign public data portals and archives websites, derived implications, and proposed 6 service plans applicable to the integrated administrative information dataset management system. This study's results will lead to utilizing the administrative datasets and the activation of services.

• Journal of the Korean BIBLIA Society for library and Information Science
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• v.31 no.2
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• pp.139-160
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• 2020

Since the Public Records Management Act was enacted in 2007, the administrative information system has already been included in the electronic records production system, and dataset has been subject to record management as a type of electronic records. With the recent revision of the enforcement decree, dataset records management has been enacted. This study analyzes business units related to administrative information systems of public institutions and examines the current status of retention periods estimation. For this purpose, we collected 36 records classification systems from 49 public institutions among the direct management agencies of the National Archives and disaster management agencies. And we discriminated 824 business units related to administrative information system and divided into large and small groups according to types. We also compared the retention period estimation of records. The problems and improvement plans of this study are expected to be used as basic data in preparing the standard of administrative dataset management in the future.