• 대한임베디드공학회논문지
• /
• 제17권5호
• /
• pp.273-280
• /
• 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.

• 한국정보통신학회논문지
• /
• 제16권2호
• /
• pp.344-349
• /
• 2012

본 논문에서는 디모자익킹 기술 개발에서 신규 맥매스터 영상 데이터에 대한 실험을 진행하고 그에 따른 해석 결과를 제공한다. 디모자익킹 기술 개발을 위해서 사용되는 실험 영상으로 현재까지 가장 알려져 있는 데이터는 코닥 영상이다. 하지만 최근 신규 영상 데이터가 개발됨으로써 이전과는 다른 디모자익킹 실험 결과를 보여주고 있다. 따라서, 본 논문에서는 코닥 영상과 맥매스터 영상에 대한 통계적 특성을 실험, 분석 하고 비교함으로써 신규 실험 영상이 어떤 특징을 갖는지에 대해서 해석한다. 또한 실험 결과와 해석은 맥매스터 영상이 디모자익킹 기술 개발에 실험 영상으로써 가치가 있음을 보여주며, 향후 개발될 디모자익킹 기술에 대한 기초 자료로 활용될 것으로 기대된다.

• 대한원격탐사학회지
• /
• 제38권1호
• /
• pp.103-110
• /
• 2022

Since satellite images generally include clouds in the atmosphere, it is essential to detect or mask clouds before satellite image processing. Clouds were detected using physical characteristics of clouds in previous research. Cloud detection methods using deep learning techniques such as CNN or the modified U-Net in image segmentation field have been studied recently. Since image segmentation is the process of assigning a label to every pixel in an image, precise pixel-based dataset is required for cloud detection. Obtaining accurate training datasets is more important than a network configuration in image segmentation for cloud detection. Existing deep learning techniques used different training datasets. And test datasets were extracted from intra-dataset which were acquired by same sensor and procedure as training dataset. Different datasets make it difficult to determine which network shows a better overall performance. To verify the effectiveness of the cloud detection network such as Cloud-Net, two types of networks were trained using the cloud dataset from KOMPSAT-3 images provided by the AIHUB site and the L8-Cloud dataset from Landsat8 images which was publicly opened by a Cloud-Net author. Test data from intra-dataset of KOMPSAT-3 cloud dataset were used for validating the network. The simulation results show that the network trained with KOMPSAT-3 cloud dataset shows good performance on the network trained with L8-Cloud dataset. Because Landsat8 and KOMPSAT-3 satellite images have different GSDs, making it difficult to achieve good results from cross-sensor validation. The network could be superior for intra-dataset, but it could be inferior for cross-sensor data. It is necessary to study techniques that show good results in cross-senor validation dataset in the future.

• 한국멀티미디어학회논문지
• /
• 제23권8호
• /
• pp.1006-1018
• /
• 2020

Dataset is getting more import to develop a learning based algorithm. Quality of the algorithm definitely depends on dataset. So we introduce new dataset over 200 thousands images which are fully labeled multi modal sensor data. Proposed dataset was designed and constructed for researchers who want to develop detection, tracking, and action classification in outdoor environment for surveillance scenarios. The dataset includes various images and multi modal sensor data under different weather and lighting condition. Therefor, we hope it will be very helpful to develop more robust algorithm for systems equipped with difference kinds of sensors in outdoor application. Case studies with the proposed dataset are also discussed in this paper.

• 한국기록관리학회지
• /
• 제5권2호
• /
• pp.103-124
• /
• 2005

정부기관 및 기업에서는 다양한 데이터세트가 생산된다. 업무활동의 일부로 생산되는 데이터세트는 현재 단순히 정보시스템 내에 저장되어 있고, 기록으로 획득되어 관리되고 있지 못하다. 원 데이터로서 업무활동에서 발생한 데이터세트 기록은, 시급히 기록관리 영역으로 편입되어 관리되지 않는다면, 오래지 않아 사장될 것이다. 데이터세트는 정부 정책 및 법제도 형성에 있어 기본 데이터로 활용 가능할 뿐 아니라 정책결정과정을 보여주는 증거로서 데이터세트 기록의 관리와 보존은 반드시 필요하다. 따라서 본 연구는 기록으로서의 데이터세트 관리 필요성을 지적하고 해외의 사례를 분석하여 데이터세트 기록관리의 기초 자료를 제공하고자 하였다.

• 로봇학회논문지
• /
• 제17권3호
• /
• pp.245-254
• /
• 2022

Sensor dataset for autonomous driving is one of the essential components as the deep learning approaches are widely used. However, most driving datasets are focused on typical environments such as sunny or cloudy. In addition, most datasets deal with color images and lidar. In this paper, we propose a driving dataset with multi-spectral images and lidar in adverse weather conditions such as snowy, rainy, smoky, and dusty. The proposed data acquisition system has 4 types of cameras (color, near-infrared, shortwave, thermal), 1 lidar, 2 radars, and a navigation sensor. Our dataset is the first dataset that handles multi-spectral cameras in adverse weather conditions. The Proposed dataset is annotated as 2D semantic labels, 3D semantic labels, and 2D/3D bounding boxes. Many tasks are available on our dataset, for example, object detection and driveable region detection. We also present some experimental results on the adverse weather dataset.

• Journal of information and communication convergence engineering
• /
• 제22권1호
• /
• pp.80-87
• /
• 2024

The Manchu language holds historical significance, but a complete dataset of Manchu script letters for training optical character recognition machine-learning models is currently unavailable. Therefore, this paper describes the process of creating a robust dataset of extracted Manchu script letters. Rather than performing automatic letter segmentation based on whitespace or the thickness of the central word stem, an image of the Manchu script was manually inspected, and one copy of the desired letter was selected as a region of interest. This selected region of interest was used as a template to match all other occurrences of the same letter within the Manchu script image. Although the dataset in this study contained only 4,000 images of five Manchu script letters, these letters were collected from twenty-eight writing styles. A full dataset of Manchu letters is expected to be obtained through this process. The collected dataset was normalized and trained using a simple convolutional neural network to verify its effectiveness.

• 사상체질의학회지
• /
• 제29권3호
• /
• pp.242-247
• /
• 2017

Objective Korean medicine Data Center (KDC) has established large-scale biological and clinical data based on Korean medicine to demonstrate and validate its theory. The aim of this study was to develop KDC teaching dataset and user guideline to improve utilization of the KDC. Method KDC teaching dataset were selected using stratified random sampling according to the Sasang constitution (SC). This dataset included 72 variables of 500 sample subjects. The user guideline described how to conducted eight statistical analysis methods using the teaching dataset. Results The KDC teaching dataset was sampled from 200(40%) Taeeumin, 125(25%) Soeumin, and 175(35%) Soyanain. It was consisted of questionnaire (basic, habit, disease, symptom), physical exam (body measurement, blood pressure), blood exam, and expert' SC diagnosis. The usage guidelines provided instruction for users to perform several statistical analysis step by step with KDC teaching dataset. Conclusion We hope that our results will contribute to enhancing KDC utilization and understanding.

• 한국기록관리학회지
• /
• 제17권3호
• /
• pp.23-47
• /
• 2017

최근 조사에 의하면 공공기관이 사용하고 있는 전체 행정정보시스템의 수는 18,000여 종이 넘는다. 공공기록물 관리에 관한 법률은 모든 행정정보 데이터세트를 기록이라고 선언하고 있지만, 현행 전자기록관리정책으로 데이터세트 기록을 관리하기는 매우 어려운 실정이다. 이 논문은 데이터세트 기록을 관리하기 위한 정책방향을 제시하기 위한 것이다. 데이터세트 기록의 진본성은 기록의 내용, 구조, 맥락뿐 아니라 외형과 기능을 함께 보존함으로써 확보되며, 데이터세트를 전자객체가 아니라 전자기록으로 관리하기 위해서는 재현성이 전제되어야 함을 강조하였다. 또한 데이터세트 기록의 재현 및 장기보존 전략으로서 에뮬레이션의 가능성을 제시하였다. 이를 토대로 데이터세트 기록의 관리 모형과 정책 방향을 제안하였다.

• 정보관리학회지
• /
• 제38권3호
• /
• pp.263-285
• /
• 2021

행정정보데이터세트 관리의 시급성과 중요성을 인식하고 실무에 적용 가능한 실효성 있는 방안을 연구하는 것이 필요하다. 특히 데이터세트 평가를 위해 데이터세트 기록을 식별하고 기록관리를 위한 기준을 정하는 일은 상세하고 구체적으로 제시될 필요가 있다. 본 연구는 공공기관에서 운영 중인 행정정보시스템 데이터세트를 대상으로 데이터세트 식별과 평가 과정을 설계하고 검증하였다. 이와 함께 평가 과정에서 기록관을 비롯한 참여 주체들의 역할을 제시하였다. 본 연구 결과를 통해 기록관에서 데이터세트 기록 관리를 위한 구체적이고 실질적인 프로세스 및 도구를 개발하기 위한 유용한 시사점을 도출하였다.