• 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.

• 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.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.67 no.4
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• pp.227-232
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• 2018

In this paper, we propose a faster object detection and tracking method using Deep Learning, UAV(unmanned aerial vehicle), Kalman filter and YOLO(You Only Look Once)v3 algorithms. The performance of the object tracking system is decided by the performance and the accuracy of object detecting and tracking algorithms. So we applied to the YOLOv3 algorithm which is the best detection algorithm now at our proposed detecting system and also used the Kalman Filter algorithm that uses a variable detection area as the tracking system. In the experiment result, we could find the proposed system is an excellent result more than a fixed area detection system.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.39 no.6
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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.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.33 no.6
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• pp.907-917
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• 2023

Recently, a self-driving car have applied deep learning technology to advanced driver assistance system can provide convenience to drivers, but it is shown deep that learning technology is vulnerable to adversarial evasion attacks. In this paper, we performed five adversarial evasion attacks, including MI-FGSM(Momentum Iterative-Fast Gradient Sign Method), targeting the object detection algorithm YOLOv5 (You Only Look Once), and measured the object detection performance in terms of mAP(mean Average Precision). In particular, we present a method applying morphology operations for YOLO to detect objects normally by removing noise and extracting boundary. As a result of analyzing its performance through experiments, when an adversarial attack was performed, YOLO's mAP dropped by at least 7.9%. The YOLO applied our proposed method can detect objects up to 87.3% of mAP performance.

• Journal of Digital Policy
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• v.2 no.4
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• pp.33-39
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• 2023

With a continuous occurrence of right-turn traffic accidents at intersections, there is an increasing demand for measures to address these incidents. In response, a technology has been developed to detect the presence of pedestrians through object detection in CCTV footage at right-turn areas and display warning messages on the screen to alert drivers. The YOLO (You Only Look Once) model, a type of object detection model, was employed to assess the performance of object detection. An algorithm was also devised to address misidentification issues and generate warning messages when pedestrians are detected. The accuracy of recognizing pedestrians or objects and outputting warning messages was measured at approximately 82%, suggesting a potential contribution to preventing right-turn accidents

• Journal of the Korean Association of Geographic Information Studies
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• v.26 no.4
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• pp.27-39
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• 2023

Detecting greenhouses from the remote sensing datasets is useful in identifying the illegal agricultural facilities and predicting the agricultural output of the greenhouses. This research proposed a methodology for automatically detecting greenhouses from a given Planetscope satellite imagery acquired in the areas of Gimje City using the deep learning technique through a series of steps. First, multiple training images with a fixed size that contain the greenhouse features were generated from the five training Planetscope satellite imagery. Next, the YOLO(You Only Look Once) model was trained using the generated training images. Finally, the greenhouse features were detected from the input Planetscope satellite image. Statistical results showed that the 76.4% of the greenhouse features were detected from the input Planetscope satellite imagery by using the trained YOLO model. In future research, the high-resolution satellite imagery with a spatial resolution less than 1m should be used to detect more greenhouse features.

• Journal of Multimedia Information System
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• v.6 no.4
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• pp.235-238
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• 2019

In real world object detection is an active research topic for understanding different objects from images. There are different models presented in past and had significant results. In this paper we are presenting vehicle logo detection using previous object detection models such as You only look once (YOLO) and Faster Region-based CNN (F-RCNN). Both the front and rear view of the vehicles were used for training and testing the proposed method. Along with deep learning an image pre-processing algorithm called perspective transformation is proposed for all the test images. Using perspective transformation, the top view images were transformed into front view images. This algorithm has higher detection rate as compared to raw images. Furthermore, YOLO model has better result as compare to F-RCNN model.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.67 no.1
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• pp.42-46
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• 2018

In this paper, we propose fast object detection method of the cars by applying YOLOv2(You Only Look Once version 2) and UAVs (Unmanned Aerial Vehicles) while on the highway. We operated Darknet, OpenCV, CUDA and Deep Learning Server(SDX-4185) for our simulation environment. YOLOv2 is recently developed fast object detection algorithm that can detect various scale objects as fast speed. YOLOv2 convolution network algorithm allows to calculate probability by one pass evaluation and predicts location of each cars, because object detection process has simple single network. In our result, we could find cars on the highway area as fast speed and we could apply to the real time.

• 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.