• Smart Structures and Systems
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• 제24권6호
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• pp.745-757
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• 2019

Road surface deteriorations such as potholes have caused motorists heavy monetary damages every year. However, effective road condition monitoring has been a continuing challenge to road owners. Depth cameras have a small field of view and can be easily affected by vehicle bouncing. Traditional image processing methods based on algorithms such as segmentation cannot adapt to varying environmental and camera scenarios. In recent years, novel object detection methods based on deep learning algorithms have produced good results in detecting typical objects, such as faces, vehicles, structures and more, even in scenarios with changing object distances, camera angles, lighting conditions, etc. Therefore, in this study, a Deep Learning Pothole Detector (DLPD) based on the deep region-based convolutional neural network is proposed for autonomous detection of potholes from images. About 900 images with potholes and road surface conditions are collected and divided into training and testing data. Parameters of the network in the DLPD are calibrated based on sensitivity tests. Then, the calibrated DLPD is trained by the training data and applied to the 215 testing images to evaluate its performance. It is demonstrated that potholes can be automatically detected with high average precision over 93%. Potholes can be differentiated from manholes by training and applying a manhole-pothole classifier which is constructed using the convolutional neural network layers in DLPD. Repeated detection of the same potholes can be prevented through feature matching of the newly detected pothole with previously detected potholes within a small region.

• Current Optics and Photonics
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• 제3권1호
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• pp.8-15
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• 2019

We describe a common optical system that merges a LADAR system, which generates a point cloud, and a more traditional imaging system operating in the LWIR, which generates image data. The optimum diameter of the entrance pupil was determined by analysis of detection ranges of the LADAR sensor, and the result was applied to design a common optical system using LADAR sensors and LWIR sensors; the performance of these sensors was then evaluated. The minimum detectable signal of the $128{\times}128-pixel$ LADAR detector was calculated as 20.5 nW. The detection range of the LADAR optical system was calculated to be 1,000 m, and according to the results, the optimum diameter of the entrance pupil was determined to be 15.7 cm. The modulation transfer function (MTF) in relation to the diffraction limit of the designed common optical system was analyzed and, according to the results, the MTF of the LADAR optical system was 98.8% at the spatial frequency of 5 cycles per millimeter, while that of the LWIR optical system was 92.4% at the spatial frequency of 29 cycles per millimeter. The detection, recognition, and identification distances of the LWIR optical system were determined to be 5.12, 2.82, and 1.96 km, respectively.

• 한국멀티미디어학회논문지
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• 제22권2호
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• pp.136-145
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• 2019

The crowded environment of a domestic pig farm is highly vulnerable to the spread of infectious diseases such as foot-and-mouth disease, and studies have been conducted to automatically analyze behavior of pigs in a crowded pig farm through a video surveillance system using a camera. Although it is required to correctly separate occluding pigs for tracking each individual pigs, extracting the boundaries of the occluding pigs fast and accurately is a challenging issue due to the complicated occlusion patterns such as X shape and T shape. In this study, we propose a fast and accurate method to separate occluding pigs not only by exploiting the characteristics (i.e., one of the fast deep learning-based object detectors) of You Only Look Once, YOLO, but also by overcoming the limitation (i.e., the bounding box-based object detector) of YOLO with the test-time data augmentation of rotation. Experimental results with two-pigs occlusion patterns show that the proposed method can provide better accuracy and processing speed than one of the state-of-the-art widely used deep learning-based segmentation techniques such as Mask R-CNN (i.e., the performance improvement over Mask R-CNN was about 11 times, in terms of the accuracy/processing speed performance metrics).

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제14권12호
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• pp.4776-4794
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• 2020

We propose a rapid adaptive learning framework for streaming object detection, called EER-ASSL. The method combines the expected error reduction (EER) dependent rollback learning and the active semi-supervised learning (ASSL) for a rapid adaptive CNN detector. Most CNN object detectors are built on the assumption of static data distribution. However, images are often noisy and biased, and the data distribution is imbalanced in a real world environment. The proposed method consists of collaborative sampling and EER-ASSL. The EER-ASSL utilizes the active learning (AL) and rollback based semi-supervised learning (SSL). The AL allows us to select more informative and representative samples measuring uncertainty and diversity. The SSL divides the selected streaming image samples into the bins and each bin repeatedly transfers the discriminative knowledge of the EER and CNN models to the next bin until convergence and incorporation with the EER rollback learning algorithm is achieved. The EER models provide a rapid short-term myopic adaptation and the CNN models an incremental long-term performance improvement. EER-ASSL can overcome noisy and biased labels in varying data distribution. Extensive experiments shows that EER-ASSL obtained 70.9 mAP compared to state-of-the-art technology such as Faster RCNN, SSD300, and YOLOv2.

• 대한방사선기술학회지:방사선기술과학
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• 제44권4호
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• pp.375-380
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• 2021

This study aim was quantitative assessment of MTFs of spectrum of the square wave chart images and Coltman chart images for 0°, 1.7°, 2.2°, 2.9°, 4.1° by using chart method. In general device was AccuRay-650 (DK Medical System, Korea) used, indirect flat panel detector(FPD) Aero (Konica, Japan) used and MATLAB R2019a (MathWorks, USA) used. The result of comparison for each angle of MTF the edge image was highest quantitatively value for MTF finding of showed the best value of 0.1 based on the frequency of 3.5 mm^-1, value of 0.1 based on the square wave was frequency of 3.0 mm^-1 and value of 0.1 based on the Coltman transform was frequency of 2.4 mm^-1. In this study it was significant that the methodology of the international Electro-technical Commission was applied mutandis by using the Fujita method within 2~3°.

• 한국산업정보학회논문지
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• 제27권1호
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• pp.11-18
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• 2022

주간 및 야간의 보행자 감지를 위해서는 다중 스펙트럼 활용이 필수적이다. 본 논문에서는 교통사고의 위험성이 높은 교차로에서 횡단보도 근처의 보행자를 24시간 검출하기 위해 컬러 카메라 및 열화상 적외선 카메라를 사용하였다. 보행자 탐지를 위해서 YOLO v5 객체 검출기를 사용하였으며 컬러 이미지와 열화상 이미지를 동시에 사용하여 감지 성능을 향상 시켰다. 제안된 시스템은 실제 횡단보도 현장에서 확보한 주·야간 다중 스펙트럼(색상 및 열화상) 보행자 데이터 셋에서 Iou 0.5 기준 0.94 mAP의 높은 성능을 보였다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제16권5호
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• pp.1671-1686
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• 2022

Face manipulation tools represented by Deepfake have threatened the security of people's biological identity information. Particularly, manipulation tools with deep learning technology have brought great challenges to Deepfake detection. There are many solutions for Deepfake detection based on traditional machine learning and advanced deep learning. However, those solutions of detectors almost have problems of poor performance when evaluated on different quality datasets. In this paper, for the sake of making high-quality Deepfake datasets, we provide a preprocessing method based on the image pixel matrix feature to eliminate similar images and the residual channel attention network (RCAN) to resize the scale of images. Significantly, we also describe a Deepfake detector named Cascaded-Hop which is based on the PixelHop++ system and the successive subspace learning (SSL) model. By feeding the preprocessed datasets, Cascaded-Hop achieves a good classification result on different manipulation types and multiple quality datasets. According to the experiment on FaceForensics++ and Celeb-DF, the AUC (area under curve) results of our proposed methods are comparable to the state-of-the-art models.

• Journal of Astronomy and Space Sciences
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• 제40권4호
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• pp.237-245
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• 2023

Laser communication has been considered as a novel method for earth observation satellites with generation of high data volume. It offers faster data transmission speeds compared to conventional radio frequency (RF) communication due to the short wavelength and narrow beam divergence. However, laser beams are refracted due to atmospheric turbulence between the ground and the satellite. Refracted laser beams, upon reaching the receiver, result in angle-of-arrival (AoA) fluctuation, inducing image dancing and wavefront distortion. These phenomena hinder signal acquisition and lead to signal loss in the course of laser communication. So, precise alignment between the transmitter and receiver is essential to guarantee effective and reliable laser communication, which is achieved by pointing, acquisition, and tracking (PAT) system. In this study, we simulate the effectiveness of tip/tilt compensation for more efficient laser communication in the satellite-ground downlink. By compensating for low-order terms using tip/tilt mirror, we verify the alleviation of AoA fluctuations under both weak and strong atmospheric turbulence conditions. And the performance of tip/tilt correction is analyzed in terms of the AoA fluctuation and collected power on the detector.

• 한국의학물리학회지:의학물리
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• 제7권1호
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• pp.65-77
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• 1996

본 연구는 2-D 와 3-D 호프만 뇌 모형 ,3-D 제젝 모형, 그리고 단일광자방출전산화단층촬영을 이용하여 인공산물을 일으키는 요인중 자료 획득 요소, 감쇠, 잡음, 산란 그리고 재구성 방식이 영상에 미치는 영향을 분석, 평가하였다. 자료 획득 요소 중 섬광 카메라의 회전 각도와 반경을 각각 변화시키면서 영상을 획득하였다. 이때 회전 반경의 변화가 작을수록 더 우수한 질의 영상을 얻을 수 있었고 회전중심으로부터 반경이 짧을수록 영상이 더 우수하였으며 이는 모형에서 거리가 멀어질수록 조준기의 분해능이 떨어지기 때문이다. 제젝 모형에서 균일한 부위를 각 조준기에 대해 알맞는 감쇠계수를 찾는데 이용하였다. $^{99m}$ Tc 을 사용했을 때 각 조준기에 대해 가장 알맞는 감쇠계수는 모두 0.12$cm^{-1}$ /로 나타났으며, 이 값으로 각각의 영상에 대해 감쇠 보정을 해주었다. 감쇠 보정 전의 제젝 모형의 균일한 부위는 감쇠로 인해 움푹 패인 선 프로파일 모양을 나타내었고, 감쇠 보정을 해줌으로서 평행한 선 프로파일을 얻을 수 있었다. 또한 감쇠 보정을 해줌으로서 영상의 질을 개선할 수 있었다. 각 시간에 따른 잡음의 영향을 관찰하기 위해 1분, 2분, 5분, 10분, 20분에 대하여 각각 자료를 얻었다. 결과에서 1분 영상이 잡음의 영향을 가장 많이 받아 영상의 질이 나빴으며 반면에 20분 영상은 잡음의 영향을 적게 받아 영상의 질이 상대적으로 가장 좋았다. 이는 자료 획득 시간을 길게하여 계수되는 양을 늘려줌으로서 Poisson 분포를 따르는 방사능 분포의 통계적 오차를 줄일 수 있기 때문인 것으로 생각한다. 이중-에너지 창, 즉 산란 부분과 $^{99m}$ Tc 의 봉우리 에너지인 140KeV 중심 20% 에너지 구별 영역을 각각 설정한 후, 자료를 얻어 산란 보정 전과 후의 영상을 비교하였다. 제젝 모형의 경우 냉구 부위와 바 패턴 부위가 산란 보정 이전에 비해 산란 보정 이후가 더 잘 식별되었고, 3-D 호프만 뇌모형의 경우 산란 보정후 영상의 질이 더 우수하게 나타났다. 결론적으로 SPECT 영상이 자료 획득을 위한 매개변수, 감쇠, 잡음, 산란 그리고 재구성 방식에 많은 영향을 받는 것으로 나타났으며 임상 적용시 유용한 SPECT 자료를 얻기 위해서 이러한 인자들을 최적화 또는 보정해 주어야 할 것으로 생각된다.

• 한국ITS학회 논문지
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• 제2권1호
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• pp.25-40
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• 2003

본 논문은 신호교차로의 정지선 검지기를 위한 수동형적외선 검지기의 검지알고리즘의 개발이다 신뢰성 있는 교통상황정보의 획득을 위하여 수동형 적외선 검지기의 기존검지영역($1.8{\times}4.0m$)에 세부검지영역을 설정하여 신호교차로에서 교통상황정보(교통량, 점유시간, 비점유시간)를 수집하였다. 기존검지영역($1.8{\times}4.0m$)의 수동형 적외선 검지기와 본 연구에서 개발한 알고리즘을 적용한 수동형 적외선 검지기를 각각 기존PIR과 제안PIR로 명명하였다. 이와 같이 개발된 알고리즘은 교통량, 점유시간, 비점유시간, 속도 및 차로변경 유무 정보를 수집할 수 있으나 본 연구에서 알고리즘의 평가는 교통량, 점유시간 및 비점유시간으로 한정하였다. 개발된 알고리즘의 수행과정과 단계별 연구내용은 다음과 같다. (1) 제안 PIR의 검지영역은 $1.8{\times}4.0m$의 영역에 $1.8{\times}0.6m$ 영역 2개(검지영역 1, 검지영역 3)와 $1.8{\times}1.78m$ 영역 1개(검지영역 2)이 다. (2) 비디오 카메라 촬영자료는 모니터 상에 수동형 적외선 검지기의 검지영역과 동일하게 영역을 설정하여 비디오 프레임 분석을 실시하였다. (3) 검지영역 1과 검지영역 3으로 점유시간, 비점유시간, 속도자료를 수집하고, 검지영역 1, 검지영역 2, 검지 영역 3의 조합으로 차로변경 유무에 대한 정보를 수집할 수 있다. 알고리즘의 현장 적용성 검토 및 알고리즘 평가를 위하여 교통량, 점유시간, 비점유시간에 대한 평균절대편차(MAD), 평균절대비율오차(MAPE)를 정확도의 비교척도로 사용하였다. 그 결과 개발된 알고리즘을 적용한 제안검지기의 효과는 기존검지기보다 우수한 것으로 나타났고, 교통량, 점유시간 및 비점유시간은 각각 53$\%$, 40$\%$, 61$\%$의 개선효과를 보였다.