• Journal of the Society of Disaster Information
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• v.18 no.2
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• pp.364-373
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• 2022

Purpose: The purpose of this paper is to develope smoke detection using AI model for detecting the initial fire in underground utility tunnels using CCTV Method: To improve detection performance of smoke which is high irregular, a deep learning model for fire detection was trained to optimize smoke detection. Also, several approaches such as dataset cleansing and gradient exploding release were applied to enhance model, and compared with results of those. Result: Results show the proposed approaches can improve the model performance, and the final model has good prediction capability according to several indexes such as mAP. However, the final model has low false negative but high false positive capacities. Conclusion: The present model can apply to smoke detection in underground utility tunnel, fixing the defect by linking between the model and the utility tunnel control system.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.35 no.12A
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• pp.1171-1180
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• 2010

In wireless sensor networks, reliable event detection is one of the most important research issues. For the reliable event detection, previous works usually assume the events are only individual objects such as tanks and soldiers. Recently, many researches focus on detection of continuous objects such as wild fire and bio-chemical material, but they merely aim at methods to reduce communication costs. Hence, we propose a reliable continuous object detection scheme. However, it might not be trivial. Unlike individual objects that could be referred as a point, a continuous object is shown in a dynamic two-dimensional diagram since it may cover a wide area and it could dynamically alter its own shape according to physical environments, e.g. geographical conditions, wind, and so on. Hence, the continuous object detection reliability can not be estimated by the indicator for individual objects. This paper newly defines the reliability indicator for continuous object detection and proposes an error recovery mechanism relying on the estimation result from the new indicator.

• Ocean and Polar Research
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• v.37 no.1
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• pp.11-22
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• 2015

In-situ measurements are labor-intensive, time-consuming, and limited in their ability to observe currents with spatial variations in the surf zone. This paper proposes an optical image-based method of measurement of currents in the surf zone. This method measures nearshore currents by tracking in time wave breaking-induced foam patches from sequential images. Foam patches in images tend to be arrayed with irregular pixel intensity values, which are likely to remain consistent for a short period of time. This irregular intensity feature of a foam patch is characterized and represented as a keypoint using an image-based object recognition method, i.e., Scale Invariant Feature Transform (SIFT). The keypoints identified by the SIFT method are traced from time sequential images to produce instantaneous velocity fields. In order to remove erroneous velocities, the instantaneous velocity fields are filtered by binding them within upper and lower limits, and averaging the velocity data in time and space with a certain interval. The measurements that are obtained by this method are comparable to the results estimated by an existing image-based method of observing currents, named the Optical Current Meter (OCM).

• The Bulletin of The Korean Astronomical Society
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• v.39 no.2
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• pp.55.1-55.1
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• 2014

on the observation with the Gemini Multi-Object Spectrograph on the Gemini-South 8.1 m telescope. The radial velocities of four ESCs do not display any sign of systematic motion, unlike the intermediate age carbon stars in NGC 6822. The ages and metallicities derived using the Lick indices show that the ESCs are old (>=8 Gyr) and metal poor ([Fe/H] <= -1.5). NGC 6822 is found to have both metal poor ($[Fe/H]{\approx}-2.0$) and metal rich ($[Fe/H]{\approx}-0.9$) star clusters within 15' (2 kpc) from the center, whereas only metal poor clusters are observed in the outer halo with r >= 20'(2.6 kpc). Based on the kinematics, old ages, and low metallicities of ESCs, we discuss the possible origin of ESCs and the formation of the outer halo of a small dwarf irregular galaxy NGC6822.

• Journal of IKEEE
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• v.24 no.4
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• pp.1148-1155
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• 2020

Fire must be extinguished as quickly as possible because they cause a lot of economic loss and take away precious human lives. Especially, the detection of smoke, which tends to be found first in fire, is of great importance. Smoke detection based on image has many difficulties in algorithm research due to the irregular shape of smoke. In this study, we introduce a new real-time smoke detection algorithm that reduces the detection of false positives generated by irregular smoke shape based on faster r-cnn of factory-installed surveillance cameras. First, we compute the global frame similarity and mean squared error (MSE) to detect the movement of smoke from the input surveillance camera. Second, we use deep learning algorithm (Faster r-cnn) to extract deferred candidate regions. Third, the extracted candidate areas for acting are finally determined using space and temporal features as smoke area. In this study, we proposed a new algorithm using the space and temporal features of global and local frames, which are well-proposed object information, to reduce false positives based on deep learning techniques. The experimental results confirmed that the proposed algorithm has excellent performance by reducing false positives of about 99.0% while maintaining smoke detection performance.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.25 no.6_1
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• pp.545-555
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• 2007

3D buildings are the most prominent feature comprising urban scene. A few of mega-cities in the globe are virtually reconstructed in photo-realistic 3D models, which becomes accessible by the public through the state-of-the-art online mapping services. A lot of research efforts have been made to develop automatic reconstruction technique of large-scale 3D building models from remotely sensed data. However, existing methods still produce irregular building polygons due to errors induced partly by uncalibrated sensor system, scene complexity and partly inappropriate sensor resolution to observed object scales. Thus, a geometric regularization technique is urgently required to rectify such irregular building polygons that are quickly captured from low sensory data. This paper aims to develop a new method for regularizing noise building outlines extracted from airborne LiDAR data, and to evaluate its performance in comparison with existing methods. These include Douglas-Peucker's polyline simplication, total least-squared adjustment, model hypothesis-verification, and rule-based rectification. Based on Minimum Description Length (MDL) principal, a new objective function, Geometric Minimum Description Length (GMDL), to regularize geometric noises is introduced to enhance the repetition of identical line directionality, regular angle transition and to minimize the number of vertices used. After generating hypothetical regularized models, a global optimum of the geometric regularity is achieved by verifying the entire solution space. A comparative evaluation of the proposed geometric regulator is conducted using both simulated and real building vectors with various levels of noise. The results show that the GMDL outperforms the selected existing algorithms at the most of noise levels.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.12 no.4
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• pp.153-159
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• 2012

For the construction of safe and clear urban environment, it is necessary that we identify the rubbish waste volume and we know the accuracy volume. In this paper, we proposed the algorithm computes the waste volume periodically for the way of waste repository standard. After stereo camera calibration, we obtained the point cloud on the surface of the object and took this as the input of the calculation algorithm of the object volume. We proposed the volume calculation algorithms based on the non-uniform triangular meshing methods and verified the validity of the algorithm through simulation and real experiments. The proposed algorithm can be used not only as the volume calculation of the waste repository but also as the general volume calculation of a three dimensional object.

• 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 Sensor Science and Technology
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• v.23 no.6
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• pp.377-382
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• 2014

Detecting and segmenting diffuse targets in laser ranging data is a critical problem for tactical reconnaissance. In this study, we propose a new method that facilitates the characterization of diffuse irregularly shaped objects using "spin images," i.e., local 2D histograms of laser returns oriented in 3D space, and a clustering process. The proposed "cluster-based spin imaging" method resolves the problem of using standard spin images for diffuse targets and it eliminates much of the computational complexity that characterizes the production of conventional spin images. The direct processing of pre-segmented laser points, including internal points that penetrate through a diffuse object's topmost surfaces, avoids some of the requirements of the approach used at present for spin image generation, while it also greatly reduces the high computational time overheads incurred by searches to find correlated images. We employed 3D airborne range data over forested terrain to demonstrate the effectiveness of this method in discriminating the different geometric structures of individual tree clusters. Our experiments showed that cluster-based spin images have the potential to separate classes in terms of different ages and portions of tree crowns.

• Journal of the Korean Society for Precision Engineering
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• v.16 no.10
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• pp.53-58
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• 1999

Even I the Electronic Speckle Pattern Interferometry(ESPI) method that measure the strain of object within wavelength of light is less visibility than Holographic Interferometry(HI) method, the merits of application, convenience and time-save have made the method practical in industry. However, the existing ESPI methods that are based on dual-exposure, real-time and time-average method have difficulties for accurate measurement, due to irregular intensity and shake of phase. Recently, in order to solve this problem, phase shifting method have been proposed. In this method, the path of reference light in interference is shifted to make improvement in distinction and precision. But this method includes too many noise, caused by the problem of relationship between object and phase. Therefore, a method to reduce noise muse be introduced. In this paper, least square fitting method is proposed. As results, the phase-map is influenced by precise phase shifting and current of notes and speckle pattern obtained by phase shifting method is improved on the existing method driven from time-average method.