• The Journal of Bigdata
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• v.7 no.1
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• pp.37-47
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• 2022

Autonomous delivery operation data is the key to driving a paradigm shift for last-mile delivery in the Corona era. To bridge the technological gap between domestic autonomous delivery robots and overseas technology-leading countries, large-scale data collection and verification that can be used for artificial intelligence training is required as the top priority. Therefore, overseas technology-leading countries are contributing to verification and technological development by opening AI training data in public data that anyone can use. In this paper, 326 objects were collected to trainn autonomous delivery robots, and artificial intelligence models such as Mask r-CNN and Yolo v3 were trained and verified. In addition, the two models were compared based on comparison and the elements required for future autonomous delivery robot research were considered.

• The Journal of the Convergence on Culture Technology
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• v.10 no.1
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• pp.617-623
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• 2024

Identifying emotions through speech poses a significant challenge due to the complex relationship between language and emotions. Our paper aims to take on this challenge by employing feature engineering to identify emotions in speech through a multimodal classification task involving both speech and text data. We evaluated two classifiers-Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM)-both integrated with a BERT-based pre-trained model. Our assessment covers various performance metrics (accuracy, F-score, precision, and recall) across different experimental setups). The findings highlight the impressive proficiency of two models in accurately discerning emotions from both text and speech data.

• Korean Journal of Remote Sensing
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• v.36 no.5_1
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• pp.653-666
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• 2020

Understanding the vegetation structure is important to manage forest resources for sustainable forest development. With the recent development of technology, it is possible to apply new technologies such as drones and deep learning to forests and use it to estimate the vegetation structure. In this study, the vegetation structure of Gongju, Samchuk, and Seoguipo area was identified by fusion of drone-optical images and LiDAR data using Artificial Neural Networks(ANNs) with the accuracy of 92.62% (Kappa value: 0.59), 91.57% (Kappa value: 0.53), and 86.00% (Kappa value: 0.63), respectively. The vegetation structure analysis technology using deep learning is expected to increase the performance of the model as the amount of information in the optical and LiDAR increases. In the future, if the model is developed with a high-complexity that can reflect various characteristics of vegetation and sufficient sampling, it would be a material that can be used as a reference data to Korea's policies and regulations by constructing a country-level vegetation structure map.

• KIPS Transactions on Software and Data Engineering
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• v.11 no.6
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• pp.255-266
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• 2022

The quality control of coarse aggregate among aggregates, which are the main ingredients of concrete, is currently carried out by SPC(Statistical Process Control) method through sampling. We construct a smart factory for manufacturing innovation by changing the quality control of coarse aggregates to inspect the coarse aggregates based on this image by acquired images through the camera instead of the current sieve analysis. First, obtained images were preprocessed, and HED(Hollistically-nested Edge Detection) which is the filter learned by deep learning segment each object. After analyzing each aggregate by image processing the segmentation result, fineness modulus and the aggregate shape rate are determined by analyzing result. The quality of aggregate obtained through the video was examined by calculate fineness modulus and aggregate shape rate and the accuracy of the algorithm was more than 90% accurate compared to that of aggregates through the sieve analysis. Furthermore, the aggregate shape rate could not be examined by conventional methods, but the content of this paper also allowed the measurement of the aggregate shape rate. For the aggregate shape rate, it was verified with the length of models, which showed a difference of ±4.5%. In the case of measuring the length of the aggregate, the algorithm result and actual length of the aggregate showed a ±6% difference. Analyzing the actual three-dimensional data in a two-dimensional video made a difference from the actual data, which requires further research.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.24 no.1
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• pp.65-74
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• 2024

In recent years, image processing technology has played a critical role in the field of autonomous driving. Among them, image recognition technology is essential for the safety and performance of autonomous vehicles. Therefore, this paper aims to develop a hybrid image recognition system to enhance the safety and performance of autonomous vehicles. In this paper, various image recognition technologies are utilized to construct a system that recognizes and tracks objects in the vehicle's surroundings. Machine learning and deep learning algorithms are employed for this purpose, and objects are identified and classified in real-time through image processing and analysis. Furthermore, this study aims to fuse image processing technology with vehicle control systems to improve the safety and performance of autonomous vehicles. To achieve this, the identified object's information is transmitted to the vehicle control system to enable appropriate autonomous driving responses. The developed hybrid image recognition system in this paper is expected to significantly improve the safety and performance of autonomous vehicles. This is expected to accelerate the commercialization of autonomous vehicles.

• Proceedings of the Korea Information Processing Society Conference
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• 2018.10a
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• pp.686-689
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• 2018

3D 손 포즈 추정(Hand Pose Estimation, HPE)은 스마트 인간 컴퓨터 인터페이스를 위해서 중요한 기술이다. 이 연구에서는 딥러닝 방법을 기반으로 하여 단일 RGB-Depth 카메라로 촬영한 양손의 3D 손 자세를 실시간으로 인식하는 손 포즈 추정 시스템을 제시한다. 손 포즈 추정 시스템은 4단계로 구성된다. 첫째, Skin Detection 및 Depth cutting 알고리즘을 사용하여 양손을 RGB와 깊이 영상에서 감지하고 추출한다. 둘째, Convolutional Neural Network(CNN) Classifier는 오른손과 왼손을 구별하는데 사용된다. CNN Classifier 는 3개의 convolution layer와 2개의 Fully-Connected Layer로 구성되어 있으며, 추출된 깊이 영상을 입력으로 사용한다. 셋째, 학습된 CNN regressor는 추출된 왼쪽 및 오른쪽 손의 깊이 영상에서 손 관절을 추정하기 위해 다수의 Convolutional Layers, Pooling Layers, Fully Connected Layers로 구성된다. CNN classifier와 regressor는 22,000개 깊이 영상 데이터셋으로 학습된다. 마지막으로, 각 손의 3D 손 자세는 추정된 손 관절 정보로부터 재구성된다. 테스트 결과, CNN classifier는 오른쪽 손과 왼쪽 손을 96.9%의 정확도로 구별할 수 있으며, CNN regressor는 형균 8.48mm의 오차 범위로 3D 손 관절 정보를 추정할 수 있다. 본 연구에서 제안하는 손 포즈 추정 시스템은 가상 현실(virtual reality, VR), 증강 현실(Augmented Reality, AR) 및 융합 현실 (Mixed Reality, MR) 응용 프로그램을 포함한 다양한 응용 분야에서 사용할 수 있다.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.2
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• pp.20-25
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• 2021

The target-object classification method was implemented using a deep-learning-based detection model in real-time images. The object detection model was a deep-learning-based detection model that allowed extensive data collection and machine learning processes to classify similar target-objects. The recognition model was implemented by changing the processing structure of the detection model and combining developed the vision-processing module. To classify the target-objects, the identity and similarity were defined and applied to the detection model. The use of the recognition model in industry was also considered by verifying the effectiveness of the recognition model using the real-time images of an actual soccer game. The detection model and the newly constructed recognition model were compared and verified using real-time images. Furthermore, research was conducted to optimize the recognition model in a real-time environment.

• KIPS Transactions on Software and Data Engineering
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• v.11 no.12
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• pp.517-524
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• 2022

Human pose estimation is a method of estimating a posture by extracting a human joint key point. When occlusion occurs, the joint key point extraction performance is lowered because the human joint is covered. The occlusion phenomenon is largely divided into three types of actions: self-contained, covered by other objects, and covered by background. In this paper, we propose an effective posture estimation method using a masking phenomenon enhancement technique. Although the posture estimation method has been continuously studied, research on the occlusion phenomenon of the posture estimation method is relatively insufficient. To solve this problem, the author proposes a data augmentation technique that intentionally masks human joints. The experimental results in this paper show that the intentional use of the blocking phenomenon enhancement technique is strong against the blocking phenomenon and the performance is increased.

• The Journal of the Convergence on Culture Technology
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• v.9 no.5
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• pp.509-515
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• 2023

Despite the continued development of alternative energies, fuel consumption is increasing. In particular, the price of gasoline fluctuates greatly according to fluctuations in international oil prices. Gas stations adjust their gasoline inventory to respond to gasoline price fluctuations. In this study, news datasets is used to analyze the gasoline consumption patterns through fluctuations of the gasoline inventory. First, collecting news datasets with web crawling. Second, summarizing news datasets using KoBART, which summarizes the Korean text datasets. Finally, preprocessing and deriving the fluctuations factors through N-Gram Language Model and TF-IDF. Through this study, it is possible to analyze and predict gasoline consumption patterns.

• The Journal of Bigdata
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• v.7 no.1
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• pp.15-27
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• 2022

In the past five years, the fatality rate of single-vehicle accidents has been 4.7 times higher than that of all accidents, so it is necessary to establish a system that can detect and respond to single-vehicle accidents immediately. The IoT(Internet of Thing)-based real-time traffic accident recognition system proposed in this study is as following. By attaching an IoT sensor which detects the impact and vehicle ingress to the guardrail, when an impact occurs to the guardrail, the image of the accident site is analyzed through artificial intelligence technology and transmitted to a rescue organization to perform quick rescue operations to damage minimization. An IoT sensor module that recognizes vehicles entering the monitoring area and detects the impact of a guardrail and an AI-based object detection module based on vehicle image data learning were implemented. In addition, a monitoring and operation module that imanages sensor information and image data in integrate was also implemented. For the validation of the system, it was confirmed that the target values were all met by measuring the shock detection transmission speed, the object detection accuracy of vehicles and people, and the sensor failure detection accuracy. In the future, we plan to apply it to actual roads to verify the validity using real data and to commercialize it. This system will contribute to improving road safety.