• 제목/요약/키워드: YOLO(You Only Look Once

검색결과 91건 처리시간 0.03초

Analysis of performance changes based on the characteristics of input image data in the deep learning-based algal detection model (딥러닝 기반 조류 탐지 모형의 입력 이미지 자료 특성에 따른 성능 변화 분석)

  • Juneoh Kim;Jiwon Baek;Jongrack Kim;Jungsu Park
    • Journal of Wetlands Research
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    • 제25권4호
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    • pp.267-273
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    • 2023
  • Algae are an important component of the ecosystem. However, the excessive growth of cyanobacteria has various harmful effects on river environments, and diatoms affect the management of water supply processes. Algal monitoring is essential for sustainable and efficient algae management. In this study, an object detection model was developed that detects and classifies images of four types of harmful cyanobacteria used for the criteria of the algae alert system, and one diatom, Synedra sp.. You Only Look Once(YOLO) v8, the latest version of the YOLO model, was used for the development of the model. The mean average precision (mAP) of the base model was analyzed as 64.4. Five models were created to increase the diversity of the input images used for model training by performing rotation, magnification, and reduction of original images. Changes in model performance were compared according to the composition of the input images. As a result of the analysis, the model that applied rotation, magnification, and reduction showed the best performance with mAP 86.5. The mAP of the model that only used image rotation, combined rotation and magnification, and combined image rotation and reduction were analyzed as 85.3, 82.3, and 83.8, respectively.

Design of YOLO-based Removable System for Pet Monitoring (반려동물 모니터링을 위한 YOLO 기반의 이동식 시스템 설계)

  • Lee, Min-Hye;Kang, Jun-Young;Lim, Soon-Ja
    • Journal of the Korea Institute of Information and Communication Engineering
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    • 제24권1호
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    • pp.22-27
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    • 2020
  • Recently, as the number of households raising pets increases due to the increase of single households, there is a need for a system for monitoring the status or behavior of pets. There are regional limitations in the monitoring of pets using domestic CCTVs, which requires a large number of CCTVs or restricts the behavior of pets. In this paper, we propose a mobile system for detecting and tracking cats using deep learning to solve the regional limitations of pet monitoring. We use YOLO (You Look Only Once), an object detection neural network model, to learn the characteristics of pets and apply them to Raspberry Pi to track objects detected in an image. We have designed a mobile monitoring system that connects Raspberry Pi and a laptop via wireless LAN and can check the movement and condition of cats in real time.

YOLO-based lane detection system (YOLO 기반 차선검출 시스템)

  • Jeon, Sungwoo;Kim, Dongsoo;Jung, Hoekyung
    • Journal of the Korea Institute of Information and Communication Engineering
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    • 제25권3호
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    • pp.464-470
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    • 2021
  • Automobiles have been used as simple means of transportation, but recently, as automobiles are rapidly becoming intelligent and smart, and automobile preferences are increasing, research on IT technology convergence is underway, requiring basic high-performance functions such as driver's convenience and safety. As a result, autonomous driving and semi-autonomous vehicles are developed, and these technologies sometimes deviate from lanes due to environmental problems, situations that cannot be judged by autonomous vehicles, and lane detectors may not recognize lanes. In order to improve the performance of lane departure from the lane detection system of autonomous vehicles, which is such a problem, this paper uses fast recognition, which is a characteristic of YOLO(You only look once), and is affected by the surrounding environment using CSI-Camera. We propose a lane detection system that recognizes the situation and collects driving data to extract the region of interest.

Analysis of Building Object Detection Based on the YOLO Neural Network Using UAV Images (YOLO 신경망 기반의 UAV 영상을 이용한 건물 객체 탐지 분석)

  • Kim, June Seok;Hong, Il Young
    • Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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    • 제39권6호
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    • pp.381-392
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    • 2021
  • In this study, we perform deep learning-based object detection analysis on eight types of buildings defined by the digital map topography standard code, leveraging images taken with UAV (Unmanned Aerial Vehicle). Image labeling was done for 509 images taken by UAVs and the YOLO (You Only Look Once) v5 model was applied to proceed with learning and inference. For experiments and analysis, data were analyzed by applying an open source-based analysis platform and algorithm, and as a result of the analysis, building objects were detected with a prediction probability of 88% to 98%. In addition, the learning method and model construction method necessary for the high accuracy of building object detection in the process of constructing and repetitive learning of training data were analyzed, and a method of applying the learned model to other images was sought. Through this study, a model in which high-efficiency deep neural networks and spatial information data are fused will be proposed, and the fusion of spatial information data and deep learning technology will provide a lot of help in improving the efficiency, analysis and prediction of spatial information data construction in the future.

A Study on Worker Risk Reduction Methods using the Deep Learning Image Processing Technique in the Turning Process (선삭공정에서 딥러닝 영상처리 기법을 이용한 작업자 위험 감소 방안 연구)

  • Bae, Yong Hwan;Lee, Young Tae;Kim, Ho-Chan
    • Journal of the Korean Society of Manufacturing Process Engineers
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    • 제20권12호
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    • pp.1-7
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    • 2021
  • The deep learning image processing technique was used to prevent accidents in lathe work caused by worker negligence. During lathe operation, when the chuck is rotated, it is very dangerous if the operator's hand is near the chuck. However, if the chuck is stopped during operation, it is not dangerous for the operator's hand to be in close proximity to the chuck for workpiece measurement, chip removal or tool change. We used YOLO (You Only Look Once), a deep learning image processing program for object detection and classification. Lathe work images such as hand, chuck rotation and chuck stop are used for learning, object detection and classification. As a result of the experiment, object detection and class classification were performed with a success probability of over 80% at a confidence score 0.5. Thus, we conclude that the artificial intelligence deep learning image processing technique can be effective in preventing incidents resulting from worker negligence in future manufacturing systems.

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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    • 제19권1호
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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.

Transfer Learning-based Object Detection Algorithm Using YOLO Network (YOLO 네트워크를 활용한 전이학습 기반 객체 탐지 알고리즘)

  • Lee, Donggu;Sun, Young-Ghyu;Kim, Soo-Hyun;Sim, Issac;Lee, Kye-San;Song, Myoung-Nam;Kim, Jin-Young
    • The Journal of the Institute of Internet, Broadcasting and Communication
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    • 제20권1호
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    • pp.219-223
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    • 2020
  • To guarantee AI model's prominent recognition rate and recognition precision, obtaining the large number of data is essential. In this paper, we propose transfer learning-based object detection algorithm for maintaining outstanding performance even when the volume of training data is small. Also, we proposed a tranfer learning network combining Resnet-50 and YOLO(You Only Look Once) network. The transfer learning network uses the Leeds Sports Pose dataset to train the network that detects the person who occupies the largest part of each images. Simulation results yield to detection rate as 84% and detection precision as 97%.

ONNX-based Runtime Performance Analysis: YOLO and ResNet (ONNX 기반 런타임 성능 분석: YOLO와 ResNet)

  • Jeong-Hyeon Kim;Da-Eun Lee;Su-Been Choi;Kyung-Koo Jun
    • The Journal of Bigdata
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    • 제9권1호
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    • pp.89-100
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    • 2024
  • In the field of computer vision, models such as You Look Only Once (YOLO) and ResNet are widely used due to their real-time performance and high accuracy. However, to apply these models in real-world environments, factors such as runtime compatibility, memory usage, computing resources, and real-time conditions must be considered. This study compares the characteristics of three deep model runtimes: ONNX Runtime, TensorRT, and OpenCV DNN, and analyzes their performance on two models. The aim of this paper is to provide criteria for runtime selection for practical applications. The experiments compare runtimes based on the evaluation metrics of time, memory usage, and accuracy for vehicle license plate recognition and classification tasks. The experimental results show that ONNX Runtime excels in complex object detection performance, OpenCV DNN is suitable for environments with limited memory, and TensorRT offers superior execution speed for complex models.

Individual Pig Detection Using Kinect Depth Information and Convolutional Neural Network (키넥트 깊이 정보와 컨볼루션 신경망을 이용한 개별 돼지의 탐지)

  • Lee, Junhee;Lee, Jonguk;Park, Daihee;Chung, Yongwha
    • The Journal of the Korea Contents Association
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    • 제18권2호
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    • pp.1-10
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    • 2018
  • Aggression among pigs adversely affects economic returns and animal welfare in intensive pigsties. Recently, some studies have applied information technology to a livestock management system to minimize the damage resulting from such anomalies. Nonetheless, detecting each pig in a crowed pigsty is still challenging problem. In this paper, we propose a new Kinect camera and deep learning-based monitoring system for the detection of the individual pigs. The proposed system is characterized as follows. 1) The background subtraction method and depth-threshold are used to detect only standing-pigs in the Kinect-depth image. 2) The standing-pigs are detected by using YOLO (You Only Look Once) which is the fastest and most accurate model in deep learning algorithms. Our experimental results show that this method is effective for detecting individual pigs in real time in terms of both cost-effectiveness (using a low-cost Kinect depth sensor) and accuracy (average 99.40% detection accuracies).

Development of YOLO-based apple quality sorter

  • Donggun Lee;Jooseon Oh;Youngtae Choi;Donggeon Lee;Hongjeong Lee;Sung-Bo Shim;Yushin Ha
    • Korean Journal of Agricultural Science
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    • 제50권3호
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    • pp.373-382
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    • 2023
  • The task of sorting and excluding blemished apples and others that lack commercial appeal is currently performed manually by human eye sorting, which not only causes musculoskeletal disorders in workers but also requires a significant amount of time and labor. In this study, an automated apple-sorting machine was developed to prevent musculoskeletal disorders in apple production workers and to streamline the process of sorting blemished and non-marketable apples from the better quality fruit. The apple-sorting machine is composed of an arm-rest, a main body, and a height-adjustable part, and uses object detection through a machine learning technology called 'You Only Look Once (YOLO)' to sort the apples. The machine was initially trained using apple image data, RoboFlow, and Google Colab, and the resulting images were analyzed using Jetson Nano. An algorithm was developed to link the Jetson Nano outputs and the conveyor belt to classify the analyzed apple images. This apple-sorting machine can immediately sort and exclude apples with surface defects, thereby reducing the time needed to sort the fruit and, accordingly, achieving cuts in labor costs. Furthermore, the apple-sorting machine can produce uniform quality sorting with a high level of accuracy compared with the subjective judgment of manual sorting by eye. This is expected to improve the productivity of apple growing operations and increase profitability.