• Title/Summary/Keyword: You Only Look Once

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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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    • v.25 no.4
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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.

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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    • v.18 no.2
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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).

Parking Lot Vehicle Counting Using a Deep Convolutional Neural Network (Deep Convolutional Neural Network를 이용한 주차장 차량 계수 시스템)

  • Lim, Kuoy Suong;Kwon, Jang woo
    • The Journal of The Korea Institute of Intelligent Transport Systems
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    • v.17 no.5
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    • pp.173-187
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    • 2018
  • This paper proposes a computer vision and deep learning-based technique for surveillance camera system for vehicle counting as one part of parking lot management system. We applied the You Only Look Once version 2 (YOLOv2) detector and come up with a deep convolutional neural network (CNN) based on YOLOv2 with a different architecture and two models. The effectiveness of the proposed architecture is illustrated using a publicly available Udacity's self-driving-car datasets. After training and testing, our proposed architecture with new models is able to obtain 64.30% mean average precision which is a better performance compare to the original architecture (YOLOv2) that achieved only 47.89% mean average precision on the detection of car, truck, and pedestrian.

Expiration Date Notification System Based on YOLO and OCR algorithms for Visually Impaired Person (YOLO와 OCR 알고리즘에 기반한 시각 장애우를 위한 유통기한 알림 시스템)

  • Kim, Min-Soo;Moon, Mi-Kyung;Han, Chang-Hee
    • The Journal of the Korea institute of electronic communication sciences
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    • v.16 no.6
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    • pp.1329-1338
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    • 2021
  • There are rarely effective methods to help visually impaired people when they want to know the expiration date of products excepted to only Braille. In this study, we developed an expiration date notification system based on YOLO and OCR for visually impaired people. The handicapped people can automatically know the expiration date of a specific product by using our system without the help of a caregiver, fast and accurately. The proposed system is worked by four different steps: (1) identification of a target product by scanning its barcode; (2) segmentation of an image area with the expiration date using YOLO; (3) classification of the expiration date by OCR: (4) notification of the expiration date by TTS. Our system showed an average classification accuracy of about 86.00% when blindfolded subjects used the proposed system in real-time. This result validates that the proposed system can be potentially used for visually impaired people.

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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    • v.50 no.3
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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.

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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    • v.20 no.1
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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%.

Face Detection Using Shapes and Colors in Various Backgrounds

  • Lee, Chang-Hyun;Lee, Hyun-Ji;Lee, Seung-Hyun;Oh, Joon-Taek;Park, Seung-Bo
    • Journal of the Korea Society of Computer and Information
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    • v.26 no.7
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    • pp.19-27
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    • 2021
  • In this paper, we propose a method for detecting characters in images and detecting facial regions, which consists of two tasks. First, we separate two different characters to detect the face position of the characters in the frame. For fast detection, we use You Only Look Once (YOLO), which finds faces in the image in real time, to extract the location of the face and mark them as object detection boxes. Second, we present three image processing methods to detect accurate face area based on object detection boxes. Each method uses HSV values extracted from the region estimated by the detection figure to detect the face region of the characters, and changes the size and shape of the detection figure to compare the accuracy of each method. Each face detection method is compared and analyzed with comparative data and image processing data for reliability verification. As a result, we achieved the highest accuracy of 87% when using the split rectangular method among circular, rectangular, and split rectangular methods.

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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    • v.24 no.1
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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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    • v.25 no.3
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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.

Efficient Object Recognition by Masking Semantic Pixel Difference Region of Vision Snapshot for Lightweight Embedded Systems (경량화된 임베디드 시스템에서 의미론적인 픽셀 분할 마스킹을 이용한 효율적인 영상 객체 인식 기법)

  • Yun, Heuijee;Park, Daejin
    • Journal of the Korea Institute of Information and Communication Engineering
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    • v.26 no.6
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    • pp.813-826
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    • 2022
  • AI-based image processing technologies in various fields have been widely studied. However, the lighter the board, the more difficult it is to reduce the weight of image processing algorithm due to a lot of computation. In this paper, we propose a method using deep learning for object recognition algorithm in lightweight embedded boards. We can determine the area using a deep neural network architecture algorithm that processes semantic segmentation with a relatively small amount of computation. After masking the area, by using more accurate deep learning algorithm we could operate object detection with improved accuracy for efficient neural network (ENet) and You Only Look Once (YOLO) toward executing object recognition in real time for lightweighted embedded boards. This research is expected to be used for autonomous driving applications, which have to be much lighter and cheaper than the existing approaches used for object recognition.