• Journal of Korea Society of Industrial Information Systems
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• v.25 no.6
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• pp.25-32
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• 2020

The seed, the most basic component, is an important factor in increasing production and efficiency in agriculture. Seeds with superior genes can be expected to improve agricultural productivity, crop survival, and reproduction. Currently, however, screening of superior seeds depends mostly on manual work, which requires a lot of time and manpower. In this paper, we propose a system that can extract the characteristics of seed phenotypes by using computer image processing technology, so that even a small number of people and a short period of time are needed to extract the characteristics of seeds. The proposed system detects individual seeds from images containing large quantities of seeds, and extracts and stores various characteristics such as representative colors, area, perimeter and roundness for each individual seed. Due to the regularity of input images, the accuracy of individual seed extraction in the proposed system is 99.12% for soybean seeds and 99.76% for rice seeds. The extracted data will be used as basic data for various data analyses that reflect the opinions of experts in the future, and will be used as basic data to determine the expressive nature of each seed.

• Korean Journal of Remote Sensing
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• v.37 no.3
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• pp.367-378
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• 2021

Field compost is a representative non-point pollution source for livestock. If the field compost flows into the water system due to rainfall, nutrients such as phosphorus and nitrogen contained in the field compost can adversely affect the water quality of the river. In this paper, we propose a method for detecting field compost using unmanned aerial vehicle images and deep learning-based semantic segmentation. Based on 39 ortho images acquired in the study area, about 30,000 data were obtained through data augmentation. Then, the accuracy was evaluated by applying the semantic segmentation algorithm developed based on U-net and the filtering technique of Open CV. As a result of the accuracy evaluation, the pixel accuracy was 99.97%, the precision was 83.80%, the recall rate was 60.95%, and the F1-Score was 70.57%. The low recall compared to precision is due to the underestimation of compost pixels when there is a small proportion of compost pixels at the edges of the image. After, It seems that accuracy can be improved by combining additional data sets with additional bands other than the RGB band.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.50-53
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• 2022

Recently, there have been cases in which counterfeit foreign ships have entered and left domestic ports several times. Vessels have a ship-specific serial number given by the International Maritime Organization (IMO) to identify the vessel, and IMO marking is mandatory on all ships built since 2004. In the case of airports and ports, which are representative logistics platforms, a security system is essential, but it is difficult to establish a security system at a port and there are many blind spots, which can cause security problems due to insufficient security systems. In this paper, a port security system is designed using deep learning object recognition and OpenCV. The security system process extracts the IMO number of the ship after recognizing the object when entering the ship, determines whether it is the same ship through feature point matching for ships with entry records, and stores the ship image and IMO number in the entry/exit DB for the first arrival vessel. Through the system of this paper, port security can be strengthened by improving the efficiency and system of port logistics by increasing the efficiency of port management personnel and reducing incidental costs caused by unauthorized entry.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.10a
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• pp.236-238
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• 2022

According to the current Road Traffic Act, the 2020 amendment bill is currently in effect as a system that designates vehicle types for each lane for the purpose of securing road use efficiency and traffic safety. When comparing the number of traffic accident fatalities per 10,000 vehicles in Germany and Korea, the number of traffic accident deaths in Germany is significantly lower than in Korea. The representative case of the German autobahn, which did not impose a speed limit, suggests that Korea's speeding laws are not the only answer to reducing the accident rate. The designated lane system, which is observed in accordance with the keep right principle of the Autobahn Expressway, plays a major role in reducing traffic accidents. Based on this fact, we propose a traffic enforcement system to crack down on vehicles violating the designated lane system and improve the compliance rate. We develop a designated lane enforcement system that recognizes vehicle types using Yolo5, a deep learning object recognition model, recognizes license plates and lanes using OpenCV, and stores the extracted data in the server to determine whether or not laws are violated.Accordingly, it is expected that there will be an effect of reducing the traffic accident rate through the improvement of driver's awareness and compliance rate.

• Journal of Internet of Things and Convergence
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• v.10 no.2
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• pp.69-75
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• 2024

This paper proposes an IoT-based indoor autonomous driving system that applies SLAM (Simultaneous Localization And Mapping) and Navigation techniques in a ROS (Robot Operating System) environment based on TurtleBot3. The proposed autonomous driving system can be applied to indoor autonomous wheelchairs and robots. In this study, the operation was verified by applying it to an indoor self-driving wheelchair. The proposed autonomous driving system provides two functions. First, indoor environment information is collected and stored, which allows the wheelchair to recognize obstacles. By performing navigation using the map created through this, the rider can move to the desired location through autonomous driving of the wheelchair. Second, it provides the ability to track and move a specific logo through image recognition using OpenCV. Through this, information services can be received from guides wearing uniforms with the organization's unique logo. The proposed system is expected to provide convenience to passengers by improving mobility, safety, and usability over existing wheelchairs.

• Korean Journal of Food Science and Technology
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• v.45 no.2
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• pp.174-179
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• 2013

Three low-molecular compounds were isolated from methanol extracts of pear (Pyrus pyrifolia N. cv. Chuhwangbae) fruit peels using solvent fractionation, various types of column chromatogrphy (Diaion HP-20, Sephadex LH-20, and silica gel), and high performance liquid chromatography with an assay guided by 1,1-diphenyl-2-picrylhydrazyl radical-scavenging activity. The isolated compounds were identified as 2-carboxyl-4(1H)-quinolinone (kynurenic acid, 1) from butanol fraction, cis-p-coumaric acid (2) from ethyl acetate-acidic fraction, and vanillin (3) from the ethyl acetate-phenolic fraction, respectively. These isolated compounds were confirmed on the basis of the spectroscopic data of electrospray ionization mass spectrometry and nuclear magnetic resonance. This is the first time that compounds 1-3 were isolated and identified in pear.

• Research in Plant Disease
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• v.24 no.2
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• pp.123-131
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• 2018

Kiwifruit has been cultivated in southern coastal regions of Korea since late 1970s. New cultivars have been successively released in recent years. In this study, we investigated major disease and pest incidences in new kiwifruit cultivars 'Goldone', 'Redvita' and 'Garmrok' at open field in Sacheon for 3 years and rain-proof field in Jeju for 2 years. For the bacterial canker, the 3 new cultivars showed more disease occurrence in Sacheon but rare in Jeju. For leaf spot disease, compared to disease incidence of 20% on 'Hayward' in Sacheon, cv. 'Garmrok' had high incidence about 60% but cv. 'Goldone' and cv. 'Redvita' had low incidence less than 20%. However, in Jeju, diseases incidences of all the new cultivars were lesser than 20%. In the case of Hemiptera, many Halyomorpha halys and Nezara antennata appeared in Sacheon but in Jeju Plautia stali was dominated. Isolated bacterial canker pathogen was identified as Pseudomonas syringae pv. actinidiae biovar 3. Leaf spots pathogens were Phomopsis sp., Phoma sp., Fusarium tricinctum and Alternaria alternata. This study shows the disease information on new kiwifruit cultivars and the adequate disease managements will be required.

• International Journal of Advanced Culture Technology
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• v.8 no.1
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• pp.207-212
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• 2020

The human centric lighting (HCL) control is a major focus point of the smart lighting system design to provide energy efficient and people mood rhythmic motivation lighting in smart buildings. This paper proposes the HCL control using indoor surveillance camera to improve the human motivation and well-beings in the indoor environments like residential and industrial buildings. In this proposed approach, the indoor surveillance camera video streams are used to predict the day lights and occupancy, occupancy specific emotional features predictions using the advanced computer vision techniques, and this human centric features are transmitted to the smart building light management system. The smart building light management system connected with internet of things (IoT) featured lighting devices and controls the light illumination of the objective human specific lighting devices. The proposed concept experimental model implemented using RGB LED lighting devices connected with IoT features open-source controller in the network along with networked video surveillance solution. The experiment results are verified with custom made automatic lighting control demon application integrated with OpenCV framework based computer vision methods to predict the human centric features and based on the estimated features the lighting illumination level and colors are controlled automatically. The experiment results received from the demon system are analyzed and used for the real-time development of a lighting system control strategy.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.3
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• pp.693-698
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• 2018

This paper proposes a vehicle detection method for parking areas using unmanned aerial vehicles (UAVs) and using YOLOv2, which is a recent, known, fast, object-detection real-time algorithm. The YOLOv2 convolutional network algorithm can calculate the probability of each class in an entire image with a one-pass evaluation, and can also predict the location of bounding boxes. It has the advantage of very fast, easy, and optimized-at-detection performance, because the object detection process has a single network. The sliding windows methods and region-based convolutional neural network series detection algorithms use a lot of region proposals and take too much calculation time for each class. So these algorithms have a disadvantage in real-time applications. This research uses the YOLOv2 algorithm to overcome the disadvantage that previous algorithms have in real-time processing problems. Using Darknet, OpenCV, and the Compute Unified Device Architecture as open sources for object detection. a deep learning server is used for the learning and detecting process with each car. In the experiment results, the algorithm could detect cars in a dense area using UAVs, and reduced overhead for object detection. It could be applied in real time.

• Environmental and Resource Economics Review
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• v.13 no.3
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• pp.387-415
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• 2004

This paper is intended to valuate air quality of the Seoul Metropolitan Area using triple-bound dichotomous choice (TBDC) contingent valuation method (CVM), supplemented by open-ended (OE) questionnaires. In the OE questionnaires, some respondents would state their willingness to pay (WTP) outside the limits of the WTP interval. It implies that WTP estimates based on the customary dichotomous choice (DC) questionnaires can be biased. We argue that the TBDC-CVM refined with OE questions is more efficient, because the latter helps purge the former of corrupted data that may have been collected by the TBDC interview process.