• Title/Summary/Keyword: YOLO

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YOLO Driving Assistance System Using Model Car (모형차를 이용한 YOLO 주행 보조 시스템)

  • Kim, Jea-gyun;Heo, Hoon;Oh, Jeong-su
    • Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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    • 2018.05a
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    • pp.671-674
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    • 2018
  • In this study, we implement a YOLO driving assistance system using a model car. The YOLO is an object detection and recognition algorithm using deep running which is becoming an issue recently. The system alerts the lane departure by applying the image processing technology to the image acquired through the camera, recognizes the objects using the YOLO, and performs various functions according to the type of the object and the distance between the vehicle. the YOLO, which is superior to the existing object detection and recognition algorithm, improves the performance of the driving assist system without additional equipment. The driving assist system using the YOLO will ensure the safety of the driver with low cost.

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Abnormal Behavior Monitoring System with YOLO AI Platform (YOLO 인공지능 플랫폼을 이용한 이상행동 감시 시스템)

  • Lee, Sang-Rak;Son, Byeong-Su;Park, Jun-Ho;Choi, Byeong-Yoon
    • Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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    • 2021.05a
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    • pp.431-433
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    • 2021
  • In this paper, abnormal behavior monitoring system using YOLO AI platform was implemented and had superior response characteristics compared to the conventional monitoring system using two-shot detection by using one-shot detection of YOLO system. The YOLO platform was trained using image dataset composed of abnormal behaviors such as assault, theft, and arson. The abnormal behavior monitoring system consists of client and server and can be applicable to smart cities to solve various crime problems if it is commercialized.

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Study of Fall Detection System of Long Short-term Memory Using Yolo-pose (Yolo-pose를 이용한 장단기 메모리의 낙상감지 시스템 연구)

  • Jeong, Seung Su;Kim, Nam Ho;Yu, Yun Seop
    • Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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    • 2022.10a
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    • pp.123-125
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    • 2022
  • In this paper, we introduce a system applied to long short-term memory using Yolo-pose. Using Yolo-pose from image data, data divided into daily life and falls are extracted and applied to LSTM for learning. In order to prevent overfitting, training is performed 8 to 2 validation and is represented by a confusion matrix. The result of Yolo-pose recorded 100% of both sensitivity and specificity, confirming that daily life and falls were well distinguished.

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YOLO, EAST : Comparison of Scene Text Detection Performance, Using a Neural Network Model (YOLO, EAST: 신경망 모델을 이용한 문자열 위치 검출 성능 비교)

  • Park, Chan Yong;Lim, Young Min;Jeong, Seung Dae;Cho, Young Heuk;Lee, Byeong Chul;Lee, Gyu Hyun;Kim, Jin Wook
    • KIPS Transactions on Software and Data Engineering
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    • v.11 no.3
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    • pp.115-124
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    • 2022
  • In this paper, YOLO and EAST models are tested to analyze their performance in text area detecting for real-world and normal text images. The earl ier YOLO models which include YOLOv3 have been known to underperform in detecting text areas for given images, but the recently released YOLOv4 and YOLOv5 achieved promising performances to detect text area included in various images. Experimental results show that both of YOLO v4 and v5 models are expected to be widely used for text detection in the filed of scene text recognition in the future.

Research of Deep Learning-Based Multi Object Classification and Tracking for Intelligent Manager System (지능형 관제시스템을 위한 딥러닝 기반의 다중 객체 분류 및 추적에 관한 연구)

  • June-hwan Lee
    • Smart Media Journal
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    • v.12 no.5
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    • pp.73-80
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    • 2023
  • Recently, intelligent control systems are developing rapidly in various application fields, and methods for utilizing technologies such as deep learning, IoT, and cloud computing for intelligent control systems are being studied. An important technology in an intelligent control system is recognizing and tracking objects in images. However, existing multi-object tracking technology has problems in accuracy and speed. In this paper, a real-time intelligent control system was implemented using YOLO v5 and YOLO v6 based on a one-shot architecture that increases the accuracy of object tracking and enables fast and accurate tracking even when objects overlap each other or when there are many objects belonging to the same class. The experiment was evaluated by comparing YOLO v5 and YOLO v6. As a result of the experiment, the YOLO v6 model shows performance suitable for the intelligent control system.

Design and Implementation of Fire Detection System Using New Model Mixing

  • Gao, Gao;Lee, SangHyun
    • International Journal of Advanced Culture Technology
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    • v.9 no.4
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    • pp.260-267
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    • 2021
  • In this paper, we intend to use a new mixed model of YoloV5 and DeepSort. For fire detection, we want to increase the accuracy by automatically extracting the characteristics of the flame in the image from the training data and using it. In addition, the high false alarm rate, which is a problem of fire detection, is to be solved by using this new mixed model. To confirm the results of this paper, we tested indoors and outdoors, respectively. Looking at the indoor test results, the accuracy of YoloV5 was 75% at 253Frame and 77% at 527Frame, and the YoloV5+DeepSort model showed the same accuracy at 75% at 253 frames and 77% at 527 frames. However, it was confirmed that the smoke and fire detection errors that appeared in YoloV5 disappeared. In addition, as a result of outdoor testing, the YoloV5 model had an accuracy of 75% in detecting fire, but an error in detecting a human face as smoke appeared. However, as a result of applying the YoloV5+DeepSort model, it appeared the same as YoloV5 with an accuracy of 75%, but it was confirmed that the false positive phenomenon disappeared.

Performance Analysis of Object Detection Method for Railway Track Equipment Based on YOLO (YOLO 기반 선로 고정장치 객체 탐지 기법의 성능 분석)

  • Junhwi Park;Changjoon Park;Namjung Kim;Jeonghwan Gwak
    • Proceedings of the Korean Society of Computer Information Conference
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    • 2023.07a
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    • pp.69-71
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    • 2023
  • 본 논문은 YOLO 기반 모델의 철도 시스템 내 선로 고정장치 탐지 성능을 비교하고 분석한다. 여기서 철도 시스템은 열차가 주행하기 위한 선로, 침목, 패스너 등의 구성요소를 포함한다. 침목은 지반과 직접적으로 연결되며, 선로를 지반 위에 안정적으로 지지하고 궤간을 정확하게 유지하는 역할을 한다. 또한, 패스너는 선로를 침목에 단단히 고정시키는 역할을 한다. 이러한 선로 고정장치의 부재는 인명 사고로 이어질 수 있어 지속적인 관리와 유지 보수가 필수적이다. 본 논문에서는 철도 시스템의 선로 고정장치 탐지를 위해 YOLO V5 및 V8 딥러닝 모델의 적용 가능성을 실험적으로 접근하며, 두 모델의 탐지 성능을 비교한다. 실험 결과, YOLO V8 및 V5 모델은 모두 뛰어난 성능을 보이는데, 특히 YOLO V8 모델이 더욱 우수한 성능을 보인다. 이로써 YOLO 알고리즘은 선로 고정장치 탐지에 적합하다는 것을 증명한다. 그러나 일부 False Positive Sample이 관측되었음을 확인하고, 이로부터 모델 성능의 개선이 필요하다는 결론을 도출하였다.

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Comparison of CNN and YOLO for Object Detection (객체 검출을 위한 CNN과 YOLO 성능 비교 실험)

  • Lee, Yong-Hwan;Kim, Youngseop
    • Journal of the Semiconductor & Display Technology
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    • v.19 no.1
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    • pp.85-92
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    • 2020
  • Object detection plays a critical role in the field of computer vision, and various researches have rapidly increased along with applying convolutional neural network and its modified structures since 2012. There are representative object detection algorithms, which are convolutional neural networks and YOLO. This paper presents two representative algorithm series, based on CNN and YOLO which solves the problem of CNN bounding box. We compare the performance of algorithm series in terms of accuracy, speed and cost. Compared with the latest advanced solution, YOLO v3 achieves a good trade-off between speed and accuracy.

AI Learning Cookie Image Data Set Construction (AI학습 맞춤형 이미지 데이터셋 구성에 대한 연구)

  • Lee, JoSun;Ko, Byeongguk;Kang, Eunsu;Choi, Hajin;Kim, Jun O;Lee, Byongkwon
    • Proceedings of the Korean Society of Computer Information Conference
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    • 2020.07a
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    • pp.347-349
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    • 2020
  • 본 논문에서는 컴퓨팅 이미지 객체인식 시스템인 YOLO 성능 향상을 위한 효율적인 이미지 마킹 정책을 제안한다. 이 정책은 이미지 데이터를 통한 객체인식 학습 YOLO의 객체인식을 높이고 다른 객체와의 구분을 최대화하여 학습 모델의 성능을 높인다. YOLO의 성능을 최대화하기 위하여 YOLO의 학습을 몇 번 시킬 것인지 무엇을 객체로 인식시킬지 동적으로 할당한다. 이때 학습 싸이클에 따라 객체의 인식이 달라지며 어느 싸이클에서 가장 효율적인지, 왜 다른 객체를 같이 학습시켜야 하는지 중명한다. 본 논문에서는 YOLO의 싸이클과 다른 객체 학습에 있어서 최적의 객체인식 싸이클과 학습 성능 향상 면에서 우수함을 보인다.

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Delelopment of Cloud-Based ERP (졸음 방지 시스템(YOLO 이용한))

  • Shin, Kwang-Seong;Shin, Seong-Yoon
    • Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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    • 2019.05a
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    • pp.153-154
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    • 2019
  • There are many jobs, not actually sleeping. Sleepy driving is one of the biggest problems in modern society. In this paper, we propose a system to control underwater guns by using deep learning (YOLO) to check eyes and to check drowsiness. So let your mind be clear.

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