• Journal of Digital Convergence
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• v.10 no.7
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• pp.123-128
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• 2012

In this paper, we propose a landmark-detection system of object for more accurate object recognition. The landmark-detection system of object becomes divided into a learning stage and a detection stage. A learning stage is created an interest-region model to set up a search region of each landmark as pre-information necessary for a detection stage and is created a detector by each landmark to detect a landmark in a search region. A detection stage sets up a search region of each landmark in an input image with an interest-region model created in the learning stage. The proposed system uses Fast Fourier Transform to detect landmark, because the landmark-detection is fast. In addition, the system fails to track objects less likely. After we developed the proposed method was applied to environment video. As a result, the system that you want to track objects moving at an irregular rate, even if it was found that stable tracking. The experimental results show that the proposed approach can achieve superior performance using various data sets to previously methods.

• Journal of Multimedia Information System
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• v.6 no.4
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• pp.203-208
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• 2019

Fast operation of a CNN based object detection is important in many application areas. It is an efficient approach to reduce the size of an input image. However, it is difficult to find an area that includes a target object with minimal computation. This paper proposes a ROI detection method that is fast and robust to noise. The proposed method is not affected by a flicker line noise that is a kind of aliasing between camera and LED light. Fast operation is achieved by using down-sampling efficiently. The accuracy of the proposed ROI detection method is 92.5% and the operation time for a frame with a resolution of 640 × 360 is 0.388msec.

• Geophysics and Geophysical Exploration
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• v.22 no.2
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• pp.72-79
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• 2019

Underwater object detection method adopting electrical resistivity technique was proposed recently, and the need of advanced data processing algorithm development counteracting various marine environmental conditions was required. In this paper, we present an improved water tank experiment system and its operation results, which can provide efficient test and verification. The main features of the system are as follows: 1) All the processes enabling real time process for not only simultaneous gathering of object images but also the electrical field measurement and visualization are carried out at 5 Hz refresh rates. 2) Data acquisition and processing for two detection lines are performed in real time to distinguish the moving direction of a target object. 3) Playback and retest functions for the saved data are equipped. 4) Through the monitoring screen, the movement of the target object and the measurement status of two detection lines can be intuitively identified. We confirmed that the enhanced physical modeling system works properly and facilitates efficient experiments.

• Proceedings of the Korea Information Processing Society Conference
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• 2021.11a
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• pp.723-726
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• 2021

현재 단일 이미지에서 Object Detection 성능은 매우 좋은 편이다. 하지만 동영상에서는 처리 속도가 너무 느리고 임베디드 시스템에서는 real-time이 힘든 상황이다. 연구 논문에서는 하이엔드 GPU에서 다른 기능 없이 YOLO만 구동했을 때 real-time이 가능하다고 하지만 실제 사용자들은 상대적으로 낮은 사양의 GPU를 사용하거나 CPU를 사용하기 때문에 일반적으로는 자연스러운 real-time을 하기가 힘들다. 본 논문에서는 이러한 제한점을 해결하고자 계산량이 많은 Object Detection model 사용을 줄이는 방안은 제시하였다. 현재 Video영상에서 Object Detection을 수행할 때 매 frame마다 YOLO모델을 구동하는 것에서 YOLO 사용을 줄임으로써 계산 효율을 높였다. 본 논문의 알고리즘은 카메라가 움직이거나 배경이 바뀌는 상황에서도 사용이 가능하다. 속도는 최소2배에서 ~10배이상까지 개선되었다.

• Proceedings of the IEEK Conference
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• 1999.06a
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• pp.615-618
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• 1999

In this paper, a object detection and tracking algorithm is presented which exhibits robust properties for image sequences with complex background. The proposed algorithm is composed of three parts: moving object detection, object tracking, and motion analysis. The moving object detection algorithm is implemented using a temporal median background method which is suitable for real-time applications. In the motion analysis, we propose a new technique for removing a temporal clutter, such as a swaying plant or a light reflection of a background object. In addition, we design a multiple vehicle tracking system based on Kalman filtering. Computer simulation of the proposed scheme shows its robustness for MPEG-7 test image sequences.

• IEMEK Journal of Embedded Systems and Applications
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• v.9 no.1
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• pp.53-59
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• 2014

In multiple object tracking, accurate detection for each of objects that appear sequentially and effective tracking in complicated cases that they are overlapped with each other are very important. In this paper, we propose a multiple object tracking system that has a concrete detection and tracking characteristics by using multi-lateral histogram and SIFT feature extraction algorithm. Especially, by limiting the matching area to object's inside and by utilizing the location informations in the keypoint matching process of SIFT algorithm, we advanced the tracking performance for multiple objects. Based on the experimental results, we found that the proposed tracking system has a robust tracking operation in the complicated environments that multiple objects are frequently overlapped in various of directions.

• Korean Journal of Remote Sensing
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• v.33 no.6_1
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• pp.1019-1027
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• 2017

As high resolution satellite images can be used, numerous studies have been carried out for exploiting these images in various fields. This paper proposes horse hoof shaped object detection method based on mathematical morphology to extract interesting targets. Interesting targets have conceptually similar shapes such as a horse hoof, not having exact size or shape. Detection of an object with the similar shapes is possible by applying mathematical morphology processes. The proposed method allows an automatic object detection system to detect the meaningful object in a large satellite image rapidly. The mathematical morphology process can be applied to binary images, and thus this method is very simple. Therefore, this method can easily extract a "horse hoof shaped object" from any image that has indistinct edges of the interesting object and different image qualities depending on the filming location, filming time, and filming environment. Using the proposed method by which a "horse hoof shaped object" can be rapidly extracted, the performance of the automatic object detection system can be improved.

• International journal of advanced smart convergence
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• v.12 no.4
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• pp.182-189
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• 2023

A recently research, object detection and segmentation have emerged as crucial technologies widely utilized in various fields such as autonomous driving systems, surveillance and image editing. This paper proposes a program that utilizes the QT framework to perform real-time object detection and precise instance segmentation by integrating YOLO(You Only Look Once) and Mask R CNN. This system provides users with a diverse image editing environment, offering features such as selecting specific modes, drawing masks, inspecting detailed image information and employing various image processing techniques, including those based on deep learning. The program advantage the efficiency of YOLO to enable fast and accurate object detection, providing information about bounding boxes. Additionally, it performs precise segmentation using the functionalities of Mask R CNN, allowing users to accurately distinguish and edit objects within images. The QT interface ensures an intuitive and user-friendly environment for program control and enhancing accessibility. Through experiments and evaluations, our proposed system has been demonstrated to be effective in various scenarios. This program provides convenience and powerful image processing and editing capabilities to both beginners and experts, smoothly integrating computer vision technology. This paper contributes to the growth of the computer vision application field and showing the potential to integrate various image processing algorithms on a user-friendly platform

• Journal of Advanced Navigation Technology
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• v.23 no.6
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• pp.495-500
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• 2019

Foreign object debris (FOD) is a generic term for various metals and non-metal foreign object and materials with potential hazards to aircraft operations. Since the method of manual FOD detection and collection in the aircraft moving area is very low in efficiency and economic efficiency, it is essential to develop to FOD automatic detection system suitable for domestic environment. This paper is the result of the performance comparison test results of the two systems for the combined operation of each optimal detection time and 95% accuracy above 100 m for complex operation using the fixed FOD automatic detection system and the mobile FOD system using EO/IR camera and radar at Taean Airfield Hanseo University. It is expected that FOD can be performed unattended through continuous R & D.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.57 no.4
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• pp.424-430
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• 2008

A new active vibration measurement system in electric power transmission line is presented, using in the nondestructive test. With a permanent magnet and a couple of coils, the system exerts impact force to a test object and in turn picks up the vibration of the object. The natural frequency with the amplitude obtained from the system are used as a basis for the detection of defects in the object. The system is controlled by an electronic device designed to facilitate the fully automated testing process with consistent repeatability and reliability which are essential to the nondestructive test. The system is expected to be applied to the wide area of defect detection including the classification of mechanical parts in production and inspection processes.