• International journal of advanced smart convergence
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• 제12권4호
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• pp.182-189
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• 2023

A recently research, object detection and segmentation have emerged as crucial technologies widely utilized in various fields such as autonomous driving systems, surveillance and image editing. This paper proposes a program that utilizes the QT framework to perform real-time object detection and precise instance segmentation by integrating YOLO(You Only Look Once) and Mask R CNN. This system provides users with a diverse image editing environment, offering features such as selecting specific modes, drawing masks, inspecting detailed image information and employing various image processing techniques, including those based on deep learning. The program advantage the efficiency of YOLO to enable fast and accurate object detection, providing information about bounding boxes. Additionally, it performs precise segmentation using the functionalities of Mask R CNN, allowing users to accurately distinguish and edit objects within images. The QT interface ensures an intuitive and user-friendly environment for program control and enhancing accessibility. Through experiments and evaluations, our proposed system has been demonstrated to be effective in various scenarios. This program provides convenience and powerful image processing and editing capabilities to both beginners and experts, smoothly integrating computer vision technology. This paper contributes to the growth of the computer vision application field and showing the potential to integrate various image processing algorithms on a user-friendly platform

• 대한임베디드공학회논문지
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• 제17권6호
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• pp.319-327
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• 2022

Transformers are the most famous deep learning models that has achieved great success in natural language processing and also showed good performance on computer vision. In this survey, we categorized transformer-based models for computer vision, particularly object detection tasks and perform comprehensive comparative experiments to understand the characteristics of each model. Next, we evaluated the models subdivided into standard transformer, with key point attention, and adding attention with coordinates by performance comparison in terms of object detection accuracy and real-time performance. For performance comparison, we used two metrics: frame per second (FPS) and mean average precision (mAP). Finally, we confirmed the trends and relationships related to the detection and real-time performance of objects in several transformer models using various experiments.

• 반도체디스플레이기술학회지
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• 제22권2호
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• pp.40-44
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• 2023

The defect detection based on deep learning requires minimal loss and high accuracy to pinpoint product defects. In this paper, we confirm the loss rate of deep learning training based on disc-shaped artificial scaffold images. It is intended to compare the performance of Cross-Entropy functions used in object detection algorithms. The model was constructed using normal, defective artificial scaffold images and category cross entropy and sparse category cross entropy. The data was repeatedly learned five times using each loss function. The average loss rate, average accuracy, final loss rate, and final accuracy according to the loss function were confirmed.

• International Journal of Internet, Broadcasting and Communication
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• 제14권4호
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• pp.58-63
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• 2022

Recently, the improvement of computational processing ability due to the rapid development of computing technology has greatly advanced the field of artificial intelligence, and research to apply it in various domains is active. In particular, in the national defense field, attention is paid to intelligent recognition among machine learning techniques, and efforts are being made to develop object identification and monitoring systems using artificial intelligence. To this end, various image processing technologies and object identification algorithms are applied to create a model that can identify friendly and enemy weapon systems and personnel in real-time. In this paper, we conducted image processing and object identification focused on tanks among various weapon systems. We initially conducted processing the tanks' image using a convolutional neural network, a deep learning technique. The feature map was examined and the important characteristics of the tanks crucial for learning were derived. Then, using YOLOv5 Network, a CNN-based object detection network, a model trained by labeling the entire tank and a model trained by labeling only the turret of the tank were created and the results were compared. The model and labeling technique we proposed in this paper can more accurately identify the type of tank and contribute to the intelligent recognition system to be developed in the future.

• 한국컴퓨터정보학회논문지
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• 제29권7호
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• pp.41-51
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• 2024

본 논문에서는 적외선 이미지에서 딥러닝 물체 탐지를 사용하여 유도무기의 표적 탐지 정확도 향상 방법을 연구한다. 적외선 이미지의 특성은 시간, 온도 등의 요인에 의해 영향을 받기 때문에 모델을 학습할 때 다양한 환경에서 표적 객체의 특징을 일관되게 표현하는 것이 중요하다. 이러한 문제를 해결하는 간단한 방법은 적절한 전처리 기술을 통해 적외선 이미지 내 표적 객체의 특징을 강조하고 노이즈를 줄이는 것이다. 그러나, 기존 연구에서는 적외선 영상 기반 딥러닝 모델 학습에서 전처리기법에 관한 충분한 논의가 이루어지지 못했다. 이에, 본 논문에서는 표적 객체 검출을 위한 적외선 이미지 기반 훈련에 대한 이미지 전처리 기술의 영향을 조사하는 것을 목표로 한다. 이를 위해 영상과 이미지의 전역(global) 또는 지역(local) 정보를 활용한 적외선 영상에 대한 전처리인 Min-max normalization, Z-score normalization, Histogram equalization, CLAHE (Contrast Limited Adaptive Histogram Equalization)에 대한 결과를 분석한다. 또한, 각 전처리 기법으로 변환된 이미지들이 객체 검출기 훈련에 미치는 영향을 확인하기 위해 다양한 전처리 방법으로 처리된 이미지에 대해 YOLOX 표적 검출기를 학습하고, 이에 대한 분석을 진행한다. 실험과 분석을 통해 전처리 기법들이 객체 검출기 정확도에 영향을 미친다는 사실을 알게 되었다. 특히, 전처리 기법 중에서도 CLAHE 기법을 사용해 실험을 진행한 결과가 81.9%의 mAP (mean average precision)을 기록하며 가장 높은 검출 정확도를 보임을 확인하였다.

• 한국군사과학기술학회지
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• 제24권6호
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• pp.591-601
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• 2021

Recently, infrared object detection(IOD) has been extensively studied due to the rapid growth of deep neural networks(DNN). Adversarial attacks using imperceptible perturbation can dramatically deteriorate the performance of DNN. However, most adversarial attack works are focused on visible image recognition(VIR), and there are few methods for IOD. We propose deep learning-based adversarial attacks for IOD by expanding several state-of-the-art adversarial attacks for VIR. We effectively validate our claim through comprehensive experiments on two challenging IOD datasets, including FLIR and MSOD.

• 한국전자통신학회논문지
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• 제15권6호
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• pp.1209-1216
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• 2020

본 연구에서는 무인항공기 원격탐사 기법과 딥러닝 기반 객체인식 알고리즘을 활용한 해안표착폐기물 탐지기법을 제안한다. 항공영상 내에 존재하는 해안표착폐기물을 탐지하기 위해 심층신경망 기반 객체 인식 알고리즘을 제안하였다. PET, 스티로폼, 기타 플라스틱의 3가지 클래스의 이미지 데이터셋으로 심층신경망 모델을 훈련시켰으며, 각 클래스별 탐지 정확도를 Darknet-53과 비교하였다. 이를 통해 해안표착 폐기물을 무인항공기를 통해 성상별 모니터링할 수 있었으며, 향후 본 연구에서 제안하는 방법이 적용될 경우 해변 전체에 대한 성상별 전수조사가 가능하며, 이를 통해 해양환경 감시 분야의 효율성 증대에 기여할 수 있을 것으로 판단된다.

• 대한원격탐사학회지
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• 제37권5_1호
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• pp.1083-1094
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• 2021

접근불능지역에 대한 핵활동 모니터링을 위해서는 고해상도 위성영상을 이용하여 핵활동 관련 객체의 변화양상을 분석하는 방법론의 수립이 필요하다. 그러나, 위성영상을 이용한 전통적인 객체탐지 및 변화탐지 기법들은 영상 취득 시 계절, 날씨 등의 영향에 의하여 탐지 결과물들을 다양한 활용분야에 적용하기에 어려움이 있다. 따라서, 본 연구에서는 딥러닝 모델을 이용하여 위성영상에서 관심객체를 탐지하고, 이를 활용하여 다시기 위성영상 내의 객체 변화를 분석하고자 하였다. 이를 위하여, 객체탐지를 위한 공개데이터셋을 이용하여 딥러닝 모델의 선행학습을 수행하고, 관심지역에 대한 학습자료를 직접 제작하여 전이학습에 적용하였다. 다시기·다종 위성영상 내의 객체를 개별적으로 탐지한 후, 이를 활용하여 영상 내 객체의 변화양상을 탐지하였다. 이를 통해 접근불능지역에 대한 핵 활동 관련 모니터링을 위하여 다양한 위성영상에 대한 객체탐지 결과를 직접적으로 변화탐지에 활용할 수 있는 가능성을 확인하였다.

• 반도체디스플레이기술학회지
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• 제20권1호
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• pp.88-93
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• 2021

The automotive industry is developing day by day. Among them, it is very important to prevent accidents while driving. However, despite the importance of developing automobile industry technology, accidents due to road defects increase every year, especially in the rainy season. To this end, we proposed a road defect detection system for road management by converging deep learning and raspberry pi, which show various possibilities. In this paper, we developed a system that visually displays through a map after analyzing the images captured by the Raspberry Pi and the route GPS. The deep learning model trained for this system achieved 96% accuracy. Through this system, it is expected to manage road defects efficiently at a low cost.

• 반도체디스플레이기술학회지
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• 제22권3호
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• pp.161-165
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• 2023

One of the difficult works in an autonomous driving system is detecting road lanes or objects in the road boundaries. Detecting and tracking a vehicle is able to play an important role on providing important information in the framework of advanced driver assistance systems such as identifying road traffic conditions and crime situations. This paper proposes a vehicle detection scheme based on deep learning to classify and tracking vehicles in a complex and diverse environment. We use the modified YOLO as the object detector and polynomial regression as object tracker in the driving video. With the experimental results, using YOLO model as deep learning model, it is possible to quickly and accurately perform robust vehicle tracking in various environments, compared to the traditional method.