• Journal of KIISE:Software and Applications
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• v.30 no.10
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• pp.973-982
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• 2003

This paper presents an efficient method for automatically detecting objects in a given image. The GHT is a robust template matching algorithm for automatic object detection in order to find objects of various shapes. Many different templates are applied by the GHT in order to find objects of various shapes and size. Every boundary detected by the GHT scan be used as an initial outline for more precise contour-finding techniques. The main weakness of the GHT is the excessive time and memory requirements. In order to overcome this drawback, the proposed algorithm uses a multiresolution search by scaling down the original image to half-sized and quarter-sized images. Using the information from the first iterative GHT on a quarter-sized image, the range of nuclear sizes is determined to limit the parameter space of the half-sized image. After the second iterative GHT on the half-sized image, nuclei are detected by the fine search and segmented with edge information which helps determine the exact boundary. The experimental results show that this method gives reduction in computation time and memory usage without loss of accuracy.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.7 no.8
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• pp.1802-1808
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• 2003

In this paper, we propose a method about the extraction of the pedestrian tracking trajectory in the road and we used the method of mosaic background extraction and adaptive neural network for automatic pedestrian tracking system. We used mosaic background extraction to overcome ghost phenomenon. And we detected pedestrian using differential image analysis. We used adaptive neural network for multiple pedestrian tracking that non­rigid form moving. The ART2 network is capable of detecting the mass­centers of moving objects within one frame. The history of neurons positions in the sequential frames approximates the traces of the targets. The experiments done with the network in simulated environment show promising results.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.39 no.3
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• pp.399-407
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• 2019

In Korea, the construction industry has been known to have the highest risk of safety accidents compared to other industries. Therefore, in order to improve safety in the construction industry, several researches have been carried out from the past. This study aims at improving safety of labors in construction site by constructing an effective automatic safety helmet detection system using object detection algorithm based on image data of construction field. Deep learning was conducted using Region-based Fully Convolutional Network (R-FCN) which is one of the object detection algorithms based on Convolutional Neural Network (CNN) with Transfer Learning technique. Learning was conducted with 1089 images including human and safety helmet collected from ImageNet and the mean Average Precision (mAP) of the human and the safety helmet was measured as 0.86 and 0.83, respectively.

• The Journal of the Acoustical Society of Korea
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• v.37 no.2
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• pp.118-128
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• 2018

In this paper, we have studied how to search an underwater object by learning the image generated by the side scan sonar in the convolution neural network. In the method of human side analysis of the side scan image or the image, the convolution neural network algorithm can enhance the efficiency of the analysis. The image data of the side scan sonar used in the experiment is the public data of NSWC (Naval Surface Warfare Center) and consists of four kinds of synthetic underwater objects. The convolutional neural network algorithm is based on Faster R-CNN (Region based Convolutional Neural Networks) learning based on region of interest and the details of the neural network are self-organized to fit the data we have. The results of the study were compared with a precision-recall curve, and we investigated the applicability of underwater object detection in convolution neural networks by examining the effect of change of region of interest assigned to sonar image data on detection performance.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2017.11a
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• pp.88-90
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• 2017

현재 한국해양과학기술원에서는 선박비행체 탑재용 복합센서를 개발 및 시험 적용 중에 있다. 그러나 얻어진 영상 데이터를 통해서는 목표물에 대한 정확한 위치 정보를 파악할 수 없다. 또한 크기가 큰 물체도 거리가 멀면 영상에선 작아 보이기 때문에 목표물의 크기 또한 파악하기 힘들다. 이를 보완하기 위해 본 연구에서는 복합센서를 통해 획득한 영상에 대해 warping 및 기하보정, 선박 및 익수자 자동 탐지 알고리듬, 위치 및 계수 정보 산출에 대해 소개한다. 또한 실제 실험을 통해 해당 알고리듬을 검증하였다.

• Journal of the Institute of Electronics and Information Engineers
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• v.52 no.12
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• pp.150-157
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• 2015

Pedestrian detection has largely been researched as one of the important technologies for autonomous driving vehicle and preventing accidents. There are two categories for pedestrian detection, camera-based and LIDAR-based. LIDAR-based methods have the advantage of the wide angle of view and insensitivity of illuminance change while camera-based methods have not. However, there are several problems with 3D LIDAR, such as insufficient resolution to detect distant pedestrians and decrease in detection rate in a complex situation due to segmentation error and occlusion. In this paper, two methods using GM-PHD filter are proposed to improve the poor rates of pedestrian detection algorithms based on 3D LIDAR. First one improves detection performance and resolution of object by automatic accumulation of points in previous frames onto current objects. Second one additionally enhances the detection results by applying the GM-PHD filter which is modified in order to handle the poor situation to classified multi target. A quantitative evaluation with autonomously acquired road environment data shows the proposed methods highly increase the performance of existing pedestrian detection algorithms.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.21 no.1
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• pp.215-222
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• 2017

An active synthetic aperture sonar mounted on small UUV is generated various trajectory disturbances in the traveling path by the influence of external underwater environments. That is the phase mismatch occurs in the synthetic aperture processing of the signals reflected from seabed objects and fetches the detection performance decreases. In this paper, we compensated deteriorated images by the active SAS autofocus processing using DPC and analyzed the effects of detection performance when the periodic trajectory disturbances occur in the side direction at a constant velocity and straight movement of UUV. Through simulations, the deteriorated images according to the periodic disturbance magnitudes and period variations in the platform were compensated using difference phases processing of the overlapping displaced phase centers on the adjacent transmitted ping signals, and we conformed the improved performance characteristics of azimuth resolution and detection images at 3dB reference point.

• Proceedings of the Korean Information Science Society Conference
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• 2001.04b
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• pp.571-573
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• 2001

대부분 영상검지기는 입력영상 전체영역의 밝기값에 기반하여 카메라가 받아들이는 광량을조절하는 자동 아이리스 방법을 채택하고 있다. 대형차량의 출현, 갑작스런 외부 일광의 변화가 있을 때 영상내 밝기값이 급격히 변할 수 있는데 기존 방법의 경우 기계적인 대응으로 인한 지연 때문에 차량을 미탐지하는 오류가 발생할 수 있다. 본 논문에서는 이러한 문제점을 해결하기 위해 사용자 제어 아이리스(UCI: User-Controlled Iris) 방법을 제안한다. 사용자 제어 아이리스를 사용할 경우 배경영상의 밝기값 변화에만 반응함으로써 움직이는 물체의 밝기값 또는 외부 일광이 급변하는 상황하에서도 양질의 입력영상을 얻을 수 있어 견고한 차량 탐지가 가능하다.

• Journal of the Korea Convergence Society
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• v.12 no.10
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• pp.211-218
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• 2021

A recent spread of personal mobility devices such as electric kickboards has brought about a rapid increase in accident cases. Such vehicles are susceptible to falling accidents due to their low dynamic stability and lack of outer protection chassis. This paper presents the development of an automatic emergency braking system and a safe starting system as driving assistance devices for electric kickboards. The braking system employed artificial intelligence to detect nearby threaening objects. The starting system was developed to disable powder to the motor until when the driver's boarding is confirmed. This study is meaningful in that it proposes the convergence technology of advanced driver assistance systems specialized for personal mobility devices.

• Proceedings of the Korea Information Processing Society Conference
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• 2019.10a
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• pp.928-930
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• 2019

방송에서는 당사자의 동의 없이 얼굴을 노출 시키거나, 유해물질로 판단되는 물체의 노출을 금지하고 있다. 기존의 처리방식으로 편집자가 촬영된 영상을 직접 편집하거나, 촬영 시 가리개를 이용하는 방법을 사용한다. 이러한 방법은 번거롭고, 실수로 인해 얼굴이나 유해물질이 방송에 그대로 노출될 수 있다. 본 논문에서는 딥러닝 기반의 객체탐지 모델과 동일인 판단 모델을 사용하여 편집 과정을 자동으로 처리하고 후처리뿐만 아니라 실시간 방송에서의 적용을 위해 추가적으로 객체추적 알고리즘을 도입하여 처리속도를 높이는 방안을 제시한다.