• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.5
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• pp.537-542
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• 2021

This paper improves the possibility of recognizing fast-moving objects through the YOLO (You Only Look Once) deep learning recognition model in an application environment for object recognition in images. The purpose was to study the method of collecting semantic data through processing. In the recognition model, the moving object recognition error was identified as unrecognized because of the difference between the frame rate of the camera and the moving speed of the object and a misrecognition due to the existence of a similar object in an environment adjacent to the object. To minimize the recognition errors by compensating for errors, such as unrecognized and misrecognized objects through the proposed data collection method, and applying vision processing technology for the causes of errors that may occur in images acquired for sports (tennis games) that can represent real similar environments. The effectiveness of effective secondary data collection was improved by research on methods and processing structures. Therefore, by applying the data collection method proposed in this study, ordinary people can collect and manage data to improve their health and athletic performance in the sports and health industry through the simple shooting of a smart-phone camera.

• Journal of Bio-Environment Control
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• v.31 no.3
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• pp.255-260
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• 2022

This study aimed to develop a service model that uses a deep learning algorithm for detecting diseases and pests in strawberries through image data. In addition, the pest detection performance of deep learning models was further improved by proposing segmented image data sets specialized in disease and pest symptoms. The CNN-based YOLO deep learning model was selected to enhance the existing R-CNN-based model's slow learning speed and inference speed. A general image data set and a proposed segmented image dataset was prepared to train the pest and disease detection model. When the deep learning model was trained with the general training data set, the pest detection rate was 81.35%, and the pest detection reliability was 73.35%. On the other hand, when the deep learning model was trained with the segmented image dataset, the pest detection rate increased to 91.93%, and detection reliability was increased to 83.41%. This study concludes with the possibility of improving the performance of the deep learning model by using a segmented image dataset instead of a general image dataset.

• Geophysics and Geophysical Exploration
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• v.26 no.4
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• pp.211-228
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• 2023

Ground-penetrating radar (GPR) surveys are commonly used to monitor embankments, which is a nondestructive geophysical method. The results of GPR surveys can be complex, depending on the situation, and data processing and interpretation are subject to expert experiences, potentially resulting in false detection. Additionally, this process is time-intensive. Consequently, various studies have been undertaken to detect cavities in GPR survey data using deep learning methods. Deep-learning-based approaches require abundant data for training, but GPR field survey data are often scarce due to cost and other factors constaining field studies. Therefore, in this study, a deep- learning-based model was developed for embankment GPR survey cavity detection using data augmentation strategies. A dataset was constructed by collecting survey data over several years from the same embankment. A you look only once (YOLO) model, commonly used in computer vision for object detection, was employed for this purpose. By comparing and analyzing various strategies, the optimal data augmentation approach was determined. After initial model development, a stepwise process was employed, including box clustering, transfer learning, self-ensemble, and model ensemble techniques, to enhance the final model performance. The model performance was evaluated, with the results demonstrating its effectiveness in detecting cavities in embankment GPR survey data.

• Smart Media Journal
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• v.13 no.3
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• pp.9-17
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• 2024

Due to the recent pandemic and the development of ICT technology, the use of non-face-to-face and unmanned systems is expanding, and it is very important to understand emotions in communication in non-face-to-face situations. As emotion recognition methods for various facial expressions are required to understand emotions, artificial intelligence-based research is being conducted to improve facial expression emotion recognition in image data. However, existing research on facial expression emotion recognition requires high computing power and a lot of learning time because it utilizes a large amount of data to improve accuracy. To improve these limitations, this paper proposes a method of recognizing facial expressions using age and gender, which are image meta information, as a method of recognizing facial expressions with even a small amount of data. For facial expression emotion recognition, a face was detected using the Yolo Face model from the original image data, and age and gender were classified through the VGG model based on image meta information, and then seven emotions were recognized using the EfficientNet model. The accuracy of the proposed data classification learning model was higher as a result of comparing the meta-information-based data classification model with the model trained with all data.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.22 no.6
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• pp.114-123
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• 2023

With the development of computer vision technology, video sensors such as CCTV are detecting incident. However, most of the current incident have been detected based on existing fixed imaging equipment. Accordingly, there has been a limit to the detection of incident in shaded areas where the image range of fixed equipment is not reached. With the recent development of edge-computing technology, real-time analysis of mobile image information has become possible. The purpose of this study is to evaluate the possibility of detecting expressway emergencies by introducing computer vision technology to dash cam. To this end, annotation data was constructed based on 4,388 dash cam still frame data collected by the Korea Expressway Corporation and analyzed using the YOLO algorithm. As a result of the analysis, the prediction accuracy of all objects was over 70%, and the precision of traffic accidents was about 85%. In addition, in the case of mAP(mean Average Precision), it was 0.769, and when looking at AP(Average Precision) for each object, traffic accidents were the highest at 0.904, and debris were the lowest at 0.629.

• Journal of Internet of Things and Convergence
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• v.10 no.4
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• pp.9-19
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• 2024

In this paper, a real-time public transportation monitoring system is proposed. The proposed system was implemented by developing a public transportation app and utilizing optical sensors, pressure sensors, and an object detection algorithm. Additionally, a bus model was created to verify the system's functionality. The proposed real-time public transportation monitoring system has three key features. First, the app can monitor congestion levels within public transportation by detecting seat occupancy and the total number of passengers based on changes in optical and pressure sensor readings. Second, to prevent errors in the optical sensor that can occur when multiple passengers board or disembark simultaneously, we explored the possibility of using the YOLO object detection algorithm to verify the number of passengers through CCTV footage. Third, convenience is enhanced by displaying occupied seats in different colors on a separate screen. The system also allows users to check their current location, available public transportation options, and remaining time until arrival. Therefore, the proposed system is expected to offer greater convenience to public transportation users.

• Annual Conference of KIPS
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• 2019.05a
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• pp.427-430
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• 2019

Object detection is applied in various field. Autonomous driving, surveillance, OCR(optical character recognition) and aerial image etc. We will look at the algorithms that are using to object detect. These algorithms are divided into two methods. The one is R-CNN algorithms [2], [5], [6] which based on region proposal. The other is YOLO [7] and SSD [8] which are one stage object detector based on regression/classification.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2018.06a
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• pp.123-124
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• 2018

본 논문에서는 HEVC 국제표준으로 압축된 비디오에서 움직임 물체를 추적하는 방법을 제안한다. 제안하는 방법은 HEVC 비트스트림의 대부분을 차지하는 인터 프레임의 움직임 벡터 정보를 입력 데이터로 사용하는 ST-MRF(Spatio-Temporal-Markov Random Field) 모델을 기반으로 하며, ST-MRF 모델에서 발생할 수 있는 오차전파로 점진적으로 객체를 부정확하게 추적하는 것을 HEVC GOP(Group of Picture)마다 삽입되는 인트라 프레임만을 복호화 하여 픽셀 정보를 입력으로 하는 YOLO 모델과 융합시켜 보정함으로써 추적하던 객체를 잃지 않고 강건하게 추적하는 방법을 제안한다.

• Proceedings of the Korean Society of Computer Information Conference
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• 2022.07a
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• pp.343-344
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• 2022

간암에는 크게 두 종류가 있는데 하나는 간에서 생긴 종양이 악성종양으로 진행된 것이고 다른 하나는 다른 장기에서 생긴 암이 간으로 전이되는 것이다. 본 논문에서는 간에서 생긴 종양이 악성종양으로 진행되는 것을 조기 발견하고 막고자 Object Detect 모델인 YOLO v5의 다섯 가지 모델을 비교하여 악성 종양으로의 발전 가능성이 있는 간의 lesion을 찾아보았다.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2022.06a
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• pp.28-30
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• 2022

본 연구에서는 컴퓨터 비전 기반의 딥러닝 객체 인식 기술을 이용하여 속초해수욕장에서 수집한 레이더 이미지에서 선박, 섬 및 부유체에 대해 탐지(Detection), 인식(Recognition)하는 연구를 수행하였다. 2021년 8월에 수집한 레이더 영상을 이용하여 본 연구를 수행하였으며, 움직이는 물표와 섬 등을 구분하였다. 일부 환경적인 제약에 따라 에러 발생이 있었지만, 향후 현재까지 수집한 레이더 영상을 추가하여 정확도를 높일 예정이다.