• 전자공학회논문지 IE
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• 제47권3호
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• pp.1-6
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• 2010

본 논문에서는 문자나 그래픽 같은 매우 간단한 메시지만을 표출하고 있는 소형 LED 전광판에서도 동영상이 표출될 수 있도록 임베디드 시스템을 이용한 프로세서를 설계하였다. 구현 방법으로는 임베디드 시스템에서 출력되는 24Bit의 디지털 동영상을 표출할 수 있도록 하기위하여 비디오 프로세서와 LED Display Panel을 설계한 동영상 LED 전광판을 제작하였다. 감마 보정, 밝기, 색 대비조정, 스케줄 기능, 인터넷에 의한 표출영상 변환 및 저장장치를 내장하였으며, 그래픽, 동영상 등을 소형LED 전광판에서 표출할 수 있도록 Windows CE 기반의 응용 프로그램을 설계하였다.

• 대한임베디드공학회논문지
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• 제8권1호
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• pp.25-29
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• 2013

Recently ARM processor's processing power has been increasing rapidly as it has been applied to consumer electronics products. Because of its computing power and low power consumption, it is used to various embedded systems.( including vision processing systems.) Embedded linux that provides well-made platform and GUI is also a powerful tool for ARM based embedded systems. So short period to develop is one of major advantages to the ARM based embedded system. However, for real-time date processing applications such as an image processing system, ARM needs additional equipments such as FPGA that is suitable to parallel processing applications. In this paper, we developed an embedded system using ARM processor and FPGA. FPGA takes time consuming image preprocessing and numerical algorithms needs floating point arithmetic and user interface are implemented using the ARM processor. Overall processing speed of the system is 60 frames/sec of VGA images.

• 제어로봇시스템학회논문지
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• 제20권7호
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• pp.741-749
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• 2014

This paper proposes a machine vision module for PLCs (Programmable Logic Controllers). PLC is the industrial controller most widely used in factory automation system. However most of the machine vision systems are based on PC (Personal Computer). The machine vision system embedded in PLC is required to reduce the cost and improve the convenience of implementation. In this paper, we newly propose a machine vision module based on PLC. The image processing libraries are implemented and integrated with the PLC programming tool. In order to interface the libraries with ladder programming, the ladder instruction set was also designed for each vision library. By use of the developed system, PLC users can implement vision systems easily by ladder programming. The developed system was applied to sample inspection system to verify the performance. The experimental results show that the proposed system can reduce the cost of installing as well as increase the ease-of-implementation.

• 대한임베디드공학회논문지
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• 제11권3호
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• pp.193-200
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• 2016

This paper introduces an algorithm to middle-low price drone's autonomous navigation flight system using computer vision and GPS. Existing drone operative system mainly contains using methods such as, by inputting course of the path to the installed software of the particular drone in advance of the flight or following the signal that is transmitted from the controller. However, this paper introduces new algorithm that allows autonomous navigation flight system to locate specific place, specific shape of the place and specific space in an area that the user wishes to discover. Technology developed for military industry purpose was implemented on a lower-quality hobby drones without changing its hardware, and used this paper's algorithm to maximize the performance. Camera mounted on middle-low price drone will process the image which meets user's needs will look through and search for specific area of interest when the user inputs certain image of places it wishes to find. By using this algorithm, middle-low price drone's autonomous navigation flight system expect to be apply to a variety of industries.

• 대한임베디드공학회논문지
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• 제11권5호
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• pp.313-323
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• 2016

With the development of autonomous vehicle, there has been active research on advanced driver assistance system for road marking detection using vision sensor and 3D Laser scanner. However, vision sensor has the weak points that detection is difficult in situations involving severe illumination variance, such as at night, inside a tunnel or in a shaded area; and that processing time is long because of a large amount of data from both vision sensor and 3D Laser scanner. Accordingly, this paper proposes a road marking detection and classification method using single 2D Laser scanner. This method road marking detection and classification based on accumulation distance data and intensity data acquired through 2D Laser scanner. Experiments using a real autonomous vehicle in a real environment showed that calculation time decreased in comparison with 3D Laser scanner-based method, thus demonstrating the possibility of road marking type classification using single 2D Laser scanner.

• 대한임베디드공학회논문지
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• 제15권2호
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• pp.71-78
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• 2020

This paper proposes vision sensors and deep learning-based around view monitoring system for ship berthing. Ship berthing to the port requires precise relative position and relative speed information between the mooring facility and the ship. For ships of Handysize or higher, the vesselships must be docked with the help of pilots and tugboats. In the case of ships handling dangerous cargo, tug boats push the ship and dock it in the port, using the distance and velocity information receiving from the berthing aid system (BAS). However, the existing BAS is very expensive and there is a limit on the size of the vessel that can be measured. Also, there is a limitation that it is difficult to measure distance and speed when there are obstacles near the port. This paper proposes a relative distance and speed estimation system that can be used as a ship berthing assist system. The proposed system is verified by comparing the performance with the existing laser-based distance and speed measurement system through the field tests at the actual port.

• 센서학회지
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• 제28권3호
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• pp.164-170
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• 2019

In this paper, a lane detection and tracking algorithm based on vision sensors and employing a robust filter for inner edge detection is proposed for developing a lane departure warning system (LDWS). The lateral offset value was precisely calculated by applying the proposed filter for inner edge detection in the region of interest. The proposed algorithm was subsequently compared with an existing algorithm having lateral offset-based warning alarm occurrence time, and an average error of approximately 15ms was observed. Tests were also conducted to verify whether a warning alarm is generated when a driver departs from a lane, and an average accuracy of approximately 94% was observed. Additionally, the proposed LDWS was implemented as an embedded system, mounted on a test vehicle, and was made to travel for approximately 100km for obtaining experimental results. Obtained results indicate that the average lane detection rates at day time and night time are approximately 97% and 96%, respectively. Furthermore, the processing time of the embedded system is found to be approximately 12fps.

• 대한임베디드공학회논문지
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• 제19권2호
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• pp.73-81
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• 2024

Recently, mobile manipulators have been utilized in agriculture industry for weed removal and harvest automation. This paper proposes a domain adaptive fruit detection method for harvest automation, by utilizing OWL-ViT model which is an open-vocabulary object detection model. The vision-language model can detect objects based on text prompt, and therefore, it can be extended to detect objects of undefined categories. In the development of deep learning models for real-world problems, constructing a large-scale labeled dataset is a time-consuming task and heavily relies on human effort. To reduce the labor-intensive workload, we utilized a large-scale public dataset as a source domain data and employed a domain adaptation method. Adversarial learning was conducted between a domain discriminator and feature extractor to reduce the gap between the distribution of feature vectors from the source domain and our target domain data. We collected a target domain dataset in a real-like environment and conducted experiments to demonstrate the effectiveness of the proposed method. In experiments, the domain adaptation method improved the AP50 metric from 38.88% to 78.59% for detecting objects within the range of 2m, and we achieved 81.7% of manipulation success rate.

• 대한임베디드공학회논문지
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• 제11권4호
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• pp.219-225
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• 2016

Visual object tracking is one of the key tasks in computer vision. Robust trackers should address challenging issues such as fast motion, deformation, occlusion and so on. In this paper, we therefore propose a visual object tracking method that exploits inter-frame correlations of convolutional feature maps in Convolutional Neural Net (ConvNet). The proposed method predicts the location of a target by considering inter-frame spatial correlation between target location proposals in the present frame and its location in the previous frame. The experimental results show that the proposed algorithm outperforms the state-of-the-art work especially in hard-to-track sequences.

• 한국지능시스템학회:학술대회논문집
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• 한국지능시스템학회 2008년도 춘계학술대회 학술발표회 논문집
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• pp.50-53
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• 2008

본 논문에서는 복잡한 환경에서 휴머노이드 로봇의 영상기반 운동계획을 제안하였다. 먼저 영상전처리 과정을 통해 작업환경에서 경로 계획으로 최적 경로를 탐색하고, 탐색된 경로의 거리와 방향각에 따라 퍼지규칙을 적용하여 보행 프리미티브를 선택하는 운동계획방법을 제안하였다. 다양한 장애물을 갖는 복잡한 환경에서 로봇의 보행 프리미티브를 사용하여 영상기반의 운동계획이 실시간으로 수행 가능하도록 설계하였다. 제안한 운동계획방법은 임베디드 비전 시스템을 사용한 휴머노이드 로봇을 실제 제작하여 실험을 통해 성능을 검증하였다.