• Proceedings of the Korea Information Processing Society Conference
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• 2023.11a
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• pp.678-680
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• 2023

본 연구는 2022년 유네스코 인류무형유산 대표목록에 등재된 탈춤 동작을 디지털화하여 후속 세대에게 정보를 제공하는 것을 목적으로 한다. 데이터 수집은 국가무형문화제로 지정된 탈춤 단체 13개, 시도무형문화재 단체 5개에 소속된 무형문화재, 전승자 39명이 관성식 모션 캡처 장비를 착용하고, 8대의 카메라를 이용하여 수집하였다. 데이터 가공은 바운딩박스를 수행하였고, 탈춤동작 추정은 YOLO v8을 사용하였고 탈춤 동작 분류는 YOLO v8에 CNN모델을 결합하여 130개의 탈춤을 분류하였다. 연구결과, mAP-50은 0.953, mAP50-95는 0.596, Accuracy 70%를 달성하였다. 향후 학습용 데이터셋 구축량이 늘어나고, 데이터 품질이 개선된다면 탈춤 분류 성능은 더욱 개선될 것이라 기대한다.

• 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.

• Journal of Advanced Navigation Technology
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• v.27 no.5
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• pp.685-689
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• 2023

The proposed system in the study aims to detect forest fires in real-time stream data received from the drone-camera. Recently, the number of wildfires has been increasing, and also the large scaled wildfires are frequent more and more. In order to prevent forest fire damage, many experiments using the drone camera and vision analysis are actively conducted, however there were many challenges, such as network speed, pre-processing, and model performance, to detect forest fires from real-time streaming data of the flying drone. Therefore, this study applied image data processing works to capture five good image frames for vision analysis from whole streaming data and then developed the object detection model based on YOLO_v2. As the result, the classification model performance of forest fire images reached upto 93% of accuracy, and the field test for the model verification detected the forest fire with about 70% accuracy.

• IEMEK Journal of Embedded Systems and Applications
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• v.17 no.3
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• pp.167-175
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• 2022

With the recent expansion of electric kickboards and bicycle sharing services, more and more people use them. In addition, the rapid growth of the delivery business due to the COVID-19 has significantly increased the use of two-wheeled vehicles and personal mobility. As the accident rate increases, the rule related to the two-wheeled vehicles is changed to 'mandatory helmets for kickboards and single-person transportation' and was revised to prevent boarding itself without driver's license. In this paper, we propose a wearable smart safety equipment, called SafetyHelmet, that can keep helmet-wearing duty and lower the accident rate with the communication between helmets and mobile devices. To make this function available, we propose a safe driving assistance function by notifying the driver when an object that interferes with driving such as persons or other vehicles are detected by applying the YOLO v5 object detection algorithm. Therefore it is intended to provide a safer driving assistance by reducing the failure rate to identify dangers while driving single-person transportation.

• Land and Housing Review
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• v.14 no.3
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• pp.145-155
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• 2023

Recently, with the widespread adoption of Building Information Modeling (BIM) technology in the construction industry, various object detection algorithms have been used to verify errors between 3D models and actual construction elements. Since the characteristics of objects vary depending on the type of construction facility, such as buildings, bridges, and tunnels, appropriate methods for object detection technology need to be employed. Additionally, for object detection, initial object images are required, and to obtain these, various methods, such as drones and smartphones, can be used for image acquisition. The study uses a 360° camera optimized for internal tunnel imaging to capture initial images of the tunnel structures of railway and road facilities. Various object detection methodologies including the YOLO, SSD, and R-CNN algorithms are applied to detect actual objects from the captured images. And the Faster R-CNN algorithm had a higher recognition rate and mAP value than the SSD and YOLO v5 algorithms, and the difference between the minimum and maximum values of the recognition rates was small, showing equal detection ability. Considering the increasing adoption of BIM in current railway and road construction projects, this research highlights the potential utilization of 360° cameras and object detection methodologies for tunnel facility sections, aiming to expand their application in maintenance.

• Proceedings of the Korean Society of Computer Information Conference
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• 2022.07a
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• pp.343-344
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• 2022

간암에는 크게 두 종류가 있는데 하나는 간에서 생긴 종양이 악성종양으로 진행된 것이고 다른 하나는 다른 장기에서 생긴 암이 간으로 전이되는 것이다. 본 논문에서는 간에서 생긴 종양이 악성종양으로 진행되는 것을 조기 발견하고 막고자 Object Detect 모델인 YOLO v5의 다섯 가지 모델을 비교하여 악성 종양으로의 발전 가능성이 있는 간의 lesion을 찾아보았다.

• Smart Media Journal
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• v.9 no.3
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• pp.25-30
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• 2020

The purpose of this paper is to implement a deep learning-based real-time video analysis algorithm that monitors safety of workers in industrial facilities. The worker's clothes were divided into six classes according to whether workers are wearing a helmet, safety vest, and safety belt, and a total of 5,307 images were used as learning data. The experiment was performed by comparing the mAP when weight was applied according to the number of learning iterations for 645 images, using YOLO v4. It was confirmed that the mAP was the highest with 60.13% when the number of learning iterations was 6,000, and the AP with the most test sets was the highest. In the future, we plan to improve accuracy and speed by optimizing datasets and object detection model.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.12
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• pp.1762-1769
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• 2021

The final goal of this study is to develop a system that can analyze whether a wanted vehicle is a criminal vehicle from images collected from black boxes, smartphones, CCTVs, and so on. Data collection was collected using a self-developed black box. The used data in this study has used a total of 83,753 cases such as the eight vehicle types(truck, RV, passenger car, van, SUV, bus, sports car, electric vehicle) and 434 vehicle models. As a result of vehicle recognition using YOLO v5, mAP was found to be 80%. As a result of identifying the vehicle model with ReXNet using the self-developed black box, the accuracy was found to be 99%. The result was verified by surveying field police officers. These results suggest that improving the accuracy of data labeling helps to improve vehicle recognition performance.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.16 no.5
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• pp.347-353
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• 2023

Measuring the growth of fish in fish farms still uses a laborious method. This method requires a lot of labor and causes stress to the fish, which has a negative impact on mortality. To solve this problem, we propose the Fish Growth Regression System (FGRS), a system to automate the growth of fish. FGRS consists of two modules. The first is a module that detects fish based on Yolo v8, and the second consists of a module that predicts the growth of fish using fish image data and a CNN-based neural network model. As a result of the simulation, the average prediction error before learning was 134.2 days, but after learning, the average error decreased to 39.8 days. It is expected that the system proposed in this paper can be used to predict the growing date and use the growth prediction of fish to contribute to automation in fish farms, resulting in a significant reduction in labor and cost savings.

• International Journal of Advanced Culture Technology
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• v.9 no.3
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• pp.298-304
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• 2021

Currently, to prevent the spread of COVID-19 virus infection, gathering of more than 5 people in the same space is prohibited. The purpose of this paper is to measure the distance between objects using the Yolov5 model for processing real-time images with OpenCV in order to restrict the distance between several people in the same space. Also, Utilize Euclidean distance calculation method in DeepSORT and OpenCV to minimize occlusion. In this paper, to detect the distance between people, using the open-source COCO dataset is used for learning. The technique used here is using the YoloV5 model to measure the distance, utilizing DeepSORT and Euclidean techniques to minimize occlusion, and the method of expressing through visualization with OpenCV to measure the distance between objects is used. Because of this paper, the proposed distance measurement method showed good results for an image with perspective taken from a higher position than the object in order to calculate the distance between objects by calculating the y-axis of the image.