• Journal of Korean Society for Geospatial Information Science
• /
• v.21 no.3
• /
• pp.55-62
• /
• 2013

This paper aims to detect Speed Bump by using Omni-directional Camera and to suggest Real-time update scheme of Speed Bump through Vision Based Approach. In order to detect Speed Bump from sequence of camera images, noise should be removed as well as spot estimated as shape and pattern for speed bump should be detected first. Now that speed bump has a regular form of white and yellow area, we extracted speed bump on the road by applying erosion and dilation morphological operations and by using the HSV color model. By collecting huge panoramic images from the camera, we are able to detect the target object and to calculate the distance through GPS log data. Last but not least, we evaluated accuracy of obtained result and detection algorithm by implementing SLAMS (Simultaneous Localization and Mapping system).

• Journal of the Institute of Electronics Engineers of Korea CI
• /
• v.49 no.2
• /
• pp.83-89
• /
• 2012

Natural Vegetation cover, which is very important earth resource, has been significantly altered by humans in some manner. Since this has currently resulted in a significant effect on global climate, various studies on vegetation environment including forest have been performed and the results are utilized in policy decision making. Remotely sensed data can detect, identify and map vegetation cover change based on the analysis of spectral characteristics and thus are vigorously utilized for monitoring vegetation resources. Among various vegetation indices extracted from spectral reponses of remotely sensed data, NDVI is the most popular index which provides a measure of how much photosynthetically active vegetation is present in the scene. In this study, for change detection in vegetation cover, a Multi-layer Perceptron Network (MLPN) as a nonparametric approach has been designed and applied to MODIS/Aqua vegetation indices 16-day L3 global 250m SIN Grid(v005) (MYD13Q1) data. The feature vector for change detection is constructed with the direct NDVI diffenrence at a pixel as well as the differences in some subset of NDVI series data. The research covered 5 years (2006-20110) over Korean peninsular.

• KIPS Transactions on Software and Data Engineering
• /
• v.3 no.1
• /
• pp.31-36
• /
• 2014

Social network services(SNS) including Twitter and Facebook are good resources to extract various issues like public interest, trend and topic. This paper proposes a method to extract query-related issues by calculating relatedness between terms in Twitter. As a term that frequently appears near query terms should be semantically related to a query, we calculate term relatedness in retrieved documents by summing proximity that is proportional to term frequency and inversely proportional to distance between words. Then terms, relatedness of which is bigger than threshold, are extracted as query-related issues, and our system shows those issues with a connected network. By analyzing single transitions in a connected network, compound words are easily obtained.

• The Journal of the Acoustical Society of Korea
• /
• v.37 no.2
• /
• pp.118-128
• /
• 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 Acoustical Society of Korea Conference
• /
• spring
• /
• pp.175-178
• /
• 2004

선박의 수중방사소음은 다양한 기계류나 추진기 혹은 선체와 유체간의 상호 작용으로 인하여 여러 형태의 특성신호로 나타나게 된다. 이는 선박의 운용조건, 장비 회전특성 및 내부구조에 따라 스펙트럼상에 상이한 주파수로 확인됨은 물론, 신호의 출현 형태에도 다양성을 보이고 있다. 일반적으로 선박소음은 속력 종속적인 추진 계통 성분과 비종속적인 보기류 신호로 구분되나 다수의 신호성분이 혼재되어 발생기원을 분류하는 것은 복잡한 과정을 거쳐야 한다. 본 연구에서는 이러한 점을 해결하기 위해 선박의 Tonal성 신호를 자동으로 탐지하고 분류하기 위해 규준화된 스펙트로그램 상에서 연속되는 신호에 가중치를 주어 지속성 신호여부를 판별한 후에 정해진 임계치를 초과하는 성분을 Tonal로 선정하였다. 선정된 Tonal에 대해 주파수선의 대역특성 및 시간 변동성에 대한 패턴인식 방법을 적용하여 Tonal의 발생기원이 속력 종속/비종속적인지를 자동으로 판별하는 알고리즘의 유용성에 대한 결과를 기술하였다.

• The Journal of the Institute of Internet, Broadcasting and Communication
• /
• v.12 no.6
• /
• pp.141-146
• /
• 2012

This paper introduces development of multi-rotor system and vision based autonomous landing system. Multi-rotor platform is modeled by rigid body motion with Newton Euler concept. Also Multi-rotor platform is simulated and tuned by LQR control algorithm. Vision based Autonomous Landing system uses a single camera that is mounted Multi-rotor system. Augmented reality algorithm is used as marker detection algorithm and autonomous landing code is test with GCS for the precision landing.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
• /
• v.29 no.4
• /
• pp.316-319
• /
• 2018

To detect the ground moving target, forward-looking SAR images obtained from the FMCW radar can be exploited. However, the quality of the SAR image is deteriorated due to the turbulence or fluctuation because of the flight path condition during the missile movement. We herein propose an entropy-minimizing autofocus method to compensate the motion error of forward-looking SAR. In particular, owing to the geometry of the forward-looking SAR, the motion error affects the SAR image in the two-dimensional (2D) form (azimuth and time axis). Therefore, we suggest a 2D autofocus method for the motion compensation.

• Korean Journal of Remote Sensing
• /
• v.31 no.5
• /
• pp.361-370
• /
• 2015

Various algorithms such as Chronochrome(CC), Principle Component Analysis(PCA), and spectral unmixing have been studied for hyperspectral change detection. Change detection by spectral unmixing offers useful information on the nature of the change compared to the other change detection methods which provide only the locations of changes in the scene. However, hyperspectral change detection by spectral unmixing is still in an early stage. This research proposed a new approach to extract endmembers, which have identical properties in temporally different images, by Iterative Error Analysis (IEA) and Spectral Angle Mapper(SAM). The change map obtained from the difference of abundance efficiently showed the changed pixels. Simulated images generated from Compact Airborne Spectrographic Imager (CASI) and Hyperion were used for change detection, and the experimental results showed that the proposed method performed better than CC, PCA, and spectral unmixing using N-FINDR. The proposed method has the advantage of automatically extracting endmembers without prior information, and it could be applicable for the real images composed of many materials.

• The Journal of the Acoustical Society of Korea
• /
• v.36 no.1
• /
• pp.30-38
• /
• 2017

In this paper, we propose an underwater acoustic sensor fault detection method for passive sonar systems. In general, a passive sonar system displays processed results of array signals obtained from tens of the acoustic sensors as a two-dimensional image such as displays for broadband or narrowband analysis. Since detection result display in the operation software is to display the accumulated result through the array signal processing, it is difficult to determine the possibility where signal may be contaminated by the fault or failure of a single channel sensor. In this paper, accordingly, we propose a detection method based on the analysis of RMSCR (Root Mean Square Crossing-Rate), and the processing techniques for the faulty sensors are analyzed. In order to evaluate the performance of the proposed method, the precision of detecting fault sensors is measured by using signals acquired from real array being operated in several coastal areas. Besides, we compare performance of fault processing techniques. From the experiments, it is shown that the proposed method works well in underwater environments with high average RMS, and mute (set to zero) shows the best performance with regard to fault processing techniques.

• Journal of Broadcast Engineering
• /
• v.24 no.3
• /
• pp.495-505
• /
• 2019

Typical algorithms of deep learning include CNN(Convolutional Neural Networks), which are mainly used for image recognition, and RNN(Recurrent Neural Networks), which are used mainly for speech recognition and natural language processing. Among them, CNN is able to learn from filters that generate feature maps with algorithms that automatically learn features from data, making it mainstream with excellent performance in image recognition. Since then, various algorithms such as R-CNN and others have appeared in object detection to improve performance of CNN, and algorithms such as YOLO(You Only Look Once) and SSD(Single Shot Multi-box Detector) have been proposed recently. However, since these deep learning-based detection algorithms determine the success of the detection in the still images, stable object tracking and detection in the video requires separate tracking capabilities. Therefore, this paper proposes a method of combining Kalman filters into deep learning-based detection networks for improved object tracking and detection performance in the video. The detection network used YOLO v2, which is capable of real-time processing, and the proposed method resulted in 7.7% IoU performance improvement over the existing YOLO v2 network and 20 fps processing speed in FHD images.