• Proceedings of the Korean Society of Computer Information Conference
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• 2022.07a
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• pp.475-476
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• 2022

본 논문에서는 청각장애인의 사회참여성 증진 및 사회적 차별감소를 목적으로 딥러닝 영상인식 기반으로 MediaPipe 기술을 활용한 수화-텍스트 번역시스템을 설계한다. 제시하는 시스템은 실시간으로 수집된 수화 사용자의 영상정보를 통해 동작과 표정을 인식하여 텍스트로 번역함으로써 장애인과 비장애인의 원활한 의사소통 서비스를 제공하는 것을 주 목적으로한다. 향후 개선된 수화 인식 및 문장 조합을 통해 일상에서 청각장애인과 일반인의 자유로운 커뮤니케이션을 제공하는 서비스로 확장하고자한다.

• Proceedings of the Korean Society of Computer Information Conference
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• 2022.07a
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• pp.479-480
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• 2022

고속으로 차량이 주행하는 도로에서 정지 차량이나 낙하물은 큰 사고를 유발하기에 이에 대한 대처 방안이 요구되고 있다. 갑작스런 정지 차량의 경우 예상 불가능하며, 낙하물은 순찰대를 편성하여 주기적으로 수거하고 있으나 즉각적인 대응이 어렵다. 해당 문제 해결을 위해 본 논문에서는 딥러닝 실시간 객체인식기술을 적용하여 정지 차량 및 도로 위 낙하물을 인식하며 이에 대한 정보를 제공하는 시스템을 개발하였다. 실시간 객체인식 알고리즘인 YOLOX와 실시간 객체추적기술인 deepSORT 알고리즘을 데스크톱 PC에 적용하여 구현하였다. 개발한 시스템은 정지 차량 및 낙하물에 대한 인식 결과를 제공한다. 기존 설치된 CCTV 영상을 대상으로 시스템 적용이 가능하여 저비용으로 넓은 지역에 대한 도로 위험 상황 인식을 기대할 수 있다.

• Annual Conference of KIPS
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• 2018.05a
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• pp.447-448
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• 2018

라벨인식과 같은 광학 문자 인식은 영상처리를 활용한 컴퓨터 비전의 대표적인 연구분야이다. 본 연구에서는 딥러닝 기반의 라벨인식 시스템을 고안하였다, 생산 라인에 적용되는 라벨인식 시스템은 인식 속도가 중요하기 때문에 기존의 R-CNN기반의 딥러닝 신경망보다 월등히 빠른 오브젝트 검출 시스템 YOLO를 활용하여 문자를 학습 및 인식 시스템을 개발하였다. 본 시스템은 기존 시스템에 근접하는 문자인식 정확도를 제공하고 자동으로 문자영역을 검출 가능하며, 라벨의 인쇄불량을 판독하도록 하였다. 또한 개발, 배포, 적용이 한번에 가능한 프레임워크를 통하여 생산현장에서 발생하는 다양한 이미지 처리에 활용될 전망이다.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.613-617
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• 2021

According to the growth of the service industry, stresses from emotional labor workers have been emerging as a social problem, thereby so-called the Emotional Labor Protection Act was implemented in 2018. However, insufficient substantial protection systems for emotional workers emphasizes the necessity of a digital stress management system. Thus, in this paper, we suggest a stress detection system for customer service representatives based on deep learning facial expression recognition. This system consists of a real-time face detection module, an emotion classification FER module that deep-learned big data including Korean emotion images, and a monitoring module that only visualizes stress levels. We designed the system to aim to monitor stress and prevent mental illness in emotional workers.

• Annual Conference of KIPS
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• 2017.11a
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• pp.63-65
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• 2017

딥 러닝은 하드웨어의 발전과 데이터 양의 비약적 증가에 힘입어 여러 분야에서 좋은 결과를 보여 주고 있다. 본 연구는 딥 러닝의 많은 시간을 소모하는 학습단계에서 고가의 하드웨어가 아닌 저 사양의 장비를 여러 대 결합한 분산 러닝 시스템에 대한 것이다. 분산 학습 알고리즘의 핵심을 PSO를 응용한 구조이며, 제안한 시스템의 성능은 실험으로 검증하였다.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.8
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• pp.1026-1031
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• 2021

With the development of global automotive technology and the expansion of market size, demand for vehicles is increasing, which is leading to a decrease in the number of passengers on the road and an increase in the number of vehicles on the road. This causes traffic jams, and in order to solve these problems, the number of illegal vehicles continues to increase. Various technologies are being studied to crack down on these illegal activities. Previously developed systems use trigger equipment to recognize vehicles and photograph vehicles using infrared cameras to detect the number of passengers on board. In this paper, we propose a vehicle occupant detection system with deep learning model techniques without exploiting existing system-applied trigger equipment. The proposed technique proposes a system to detect vehicles by establishing triggers within images and to apply deep learning object recognition models to detect real-time boarding personnel.

• KIPS Transactions on Software and Data Engineering
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• v.10 no.5
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• pp.161-168
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• 2021

Cracks affect the robustness of infrastructures such as buildings, bridge, pavement, and pipelines. This paper presents an automated crack detection system which detect cracks in diverse surfaces. We first constructed the combined crack dataset, consists of multiple crack datasets in diverse domains presented in prior studies. Then, state-of-the-art deep learning models in computer vision tasks including VGG, ResNet, WideResNet, ResNeXt, DenseNet, and EfficientNet, were used to validate the performance of crack detection. We divided the combined dataset into train (80%) and test set (20%) to evaluate the employed models. DenseNet121 showed the highest accuracy at 96.20% with relatively low number of parameters compared to other models. Based on the validation procedures of the advanced deep learning models in crack detection task, we shed light on the cost-effective automated crack detection system which can be applied to different surfaces and structures with low computing resources.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.388-390
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• 2021

This paper introduces a system that can help visually impaired people to board a bus using an embedded board with keypad, dot matrix, lidar sensor, NFC reader, a public data portal Open API system, and deep learning algorithm (YOLOv5). The user inputs the desired bus number through the NFC reader and keypad, and then obtains the location and expected arrival time information of the bus through the Open API real-time data through the voice output entered into the system. In addition, by displaying the bus number as the dot matrix, it can help the bus driver to wait for the visually impaired, and at the same time, a deep learning algorithm (YOLOv5) recognizes the bus number that stops in real time and detects the distance to the bus with a distance detection sensor such as lidar sensor.

• Journal of the Korea Society of Computer and Information
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• v.27 no.8
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• pp.61-68
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• 2022

In this paper, we propose a system to predict the GPS trajectory of a pedestrian based on a deep learning model. Pedestrian trajectory prediction is a study that can prevent pedestrian danger and collision situations through notifications, and has an impact on business such as various marketing. In addition, it can be used not only for pedestrians but also for path prediction of unmanned transportation, which is receiving a lot of spotlight. Among various trajectory prediction methods, this paper is a study of trajectory prediction using GPS data. It is a deep learning model-based study that predicts the next route by learning the GPS trajectory of pedestrians, which is time series data. In this paper, we presented a data set construction method that allows the deep learning model to learn the GPS route of pedestrians, and proposes a trajectory prediction deep learning model that does not have large restrictions on the prediction range. The parameters suitable for the trajectory prediction deep learning model of this study are presented, and the model's test performance are presented.

• Journal of Korea Society of Industrial Information Systems
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• v.24 no.1
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• pp.23-30
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• 2019

As deep learning with the network-based algorithms evolve, artificial intelligence is rapidly growing around the world. Among them, finance is expected to be the field where artificial intelligence is most used, and many studies have been done recently. The existing financial strategy using deep-run is vulnerable to volatility because it focuses on stock price forecasts for a single stock. Therefore, this study proposes to construct ETF products constructed through portfolio methods by calculating the stocks constituting funds by using deep learning. We analyze the performance of the proposed model in the KOSPI 100 index. Experimental results showed that the proposed model showed improved results in terms of returns or volatility.