• Proceedings of the KSR Conference
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• 2006.11b
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• pp.1483-1487
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• 2006

In electric railway, image monitoring using high speed camera provides reliable, timely information of wear and geometry status, important in taking decisions for overhead contact wire maintenance. The contribution of this research is the development of a simple real-time monitoring system for use in measurement subsystem of contact wire and geometry of overhead contact wire in electric railway. The system has been consists of a high speed CMOS camera with resolution $1024{\times}1280$ pixels, line type laser source and PC-based image acquisition system with PCI Express slot. Vision acquisition software have been used in application programming interface for image acquisition, display, and storage with a frequency of sampling of 500 acquisitions per second.

• Proceedings of the KWS Conference
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• 2002.10a
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• pp.307-313
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• 2002

This paper describes a weld pool monitoring technique, which is based on the weld pool image analysis. The proposed image analysis algorithm uses machine vision techniques to extract geometrical information from the weld pool image such as maximum weld pool width, gap width and misalignment between the joint longitudinal axis and the welding wire. These can be related to the welding parameters (welding voltage and current, wire feed speed and standoff) to produce control actions necessary to ensure that the required weld quality will be achieved. The experiments have shown that the algorithm is able to produce good estimates of the weld pool geometry; however, the adjustment of the camera parameters affects the image quality and, consequently, has a great influence over the estimation.

• Korean Journal of Remote Sensing
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• v.39 no.5_4
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• pp.1069-1073
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• 2023

A research project titled as Development of Comprehensive Land Management Technology using Satellite Image Information, funded by the Ministry of Land and Transportation, is being conducted to improve the efficiency of land management and to boost satellite image utilization in the private sector. This editorial describes the introduction of the project and papers presented in this special edition.

• 제어로봇시스템학회:학술대회논문집
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• 1995.10a
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• pp.311-314
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• 1995

This paper presents a method for measuring tool wear parameters based on two dimensional image information. The tool wear images were obtained from an ITV camera with magnifying and lighting devices, and were analyzed using image processing techniques such as thresholding, noise filtering and boundary tracing. Thresholding was used to transform the captured gray scale image into a binary image for rapid sequential image processing. The threshold level was determined using a novel technique in which the brightness histograms of two concentric windows containing the tool wear image were compared. The use of noise filtering and boundary tracing to reduce the measuring errors was explored. Performance tests of the measurement precision and processing speed revealed that the direct method was highly effective in intermittent tool wear monitoring.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2012.05a
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• pp.305-306
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• 2012

• Proceedings of the KIEE Conference
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• 2004.07e
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• pp.72-75
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• 2004

In this paper, an image recognition monitoring system, which is used for forklift, is developed. In the developed system, RF wireless communication is used for remote control system, and ATMega128 is used as main controller. Additionally, monitoring system, which is used TCP/IP protocol, is adopted.

• International conference on construction engineering and project management
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• 2022.06a
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• pp.1243-1244
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• 2022

In recent years, the growing interest in off-site construction has led to factories scaling up their manufacturing and production processes in the construction sector. Consequently, continuous large-scale site monitoring in low-variability environments, such as prefabricated components production plants (precast concrete production), has gained increasing importance. Although many studies on computer vision-based site monitoring have been conducted, challenges for deploying this technology for large-scale field applications still remain. One of the issues is collecting and transmitting vast amounts of video data. Continuous site monitoring systems are based on real-time video data collection and analysis, which requires excessive computational resources and network traffic. In addition, it is difficult to integrate various object information with different sizes and scales into a single scene. Various sizes and types of objects (e.g., workers, heavy equipment, and materials) exist in a plant production environment, and these objects should be detected simultaneously for effective site monitoring. However, with the existing object detection algorithms, it is difficult to simultaneously detect objects with significant differences in size because collecting and training massive amounts of object image data with various scales is necessary. This study thus developed a large-scale site monitoring system using edge computing and a small-object detection system to solve these problems. Edge computing is a distributed information technology architecture wherein the image or video data is processed near the originating source, not on a centralized server or cloud. By inferring information from the AI computing module equipped with CCTVs and communicating only the processed information with the server, it is possible to reduce excessive network traffic. Small-object detection is an innovative method to detect different-sized objects by cropping the raw image and setting the appropriate number of rows and columns for image splitting based on the target object size. This enables the detection of small objects from cropped and magnified images. The detected small objects can then be expressed in the original image. In the inference process, this study used the YOLO-v5 algorithm, known for its fast processing speed and widely used for real-time object detection. This method could effectively detect large and even small objects that were difficult to detect with the existing object detection algorithms. When the large-scale site monitoring system was tested, it performed well in detecting small objects, such as workers in a large-scale view of construction sites, which were inaccurately detected by the existing algorithms. Our next goal is to incorporate various safety monitoring and risk analysis algorithms into this system, such as collision risk estimation, based on the time-to-collision concept, enabling the optimization of safety routes by accumulating workers' paths and inferring the risky areas based on workers' trajectory patterns. Through such developments, this continuous large-scale site monitoring system can guide a construction plant's safety management system more effectively.

• Korean Chemical Engineering Research
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• v.48 no.4
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• pp.475-482
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• 2010

In this work, soft sensor based on image anlaysis is proposed for quantitatively estimating the visual appearance of manufactured products and is applied to quality monitoring. The methodology consists of three steps; (1) textural feature extraction from product images using wavelet transform, (2) numerical estimation of the product appearance through projection of the textural features on subspace, and (3) use of latent variables of textural features (i.e., numerical estimates of product appearance). The focus of this approach is on the consistent and quantitative estimation of continuous variations in visual appearance rather than on classification into discrete classes. This approach is illustrated through the application to the estimation and monitoring of the appearance of engineered stone countertops.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.27 no.3
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• pp.323-330
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• 2009

The demand that we are monitoring security and crime of the urban facilities is increasing recently, but the using CCTV devices are expensive. In this research, we enlarge the angle of view using the Fish-eye Lens and the Photogrammetry, the efficiency of monitoring enhance. First, we carry out the calibration of the Fish-eye Lens indoors, we calculate the correction parameters, and then covert the original image-point to new image-point correcting distortion. Second, the correction program with the correction parameters can obtain the real-time correcting image. Lastly, for authorization the developed program we compare correcting-image with scanning-imge, it is showed the RMSE is 3.2pixel.

• Journal of Conservation Science
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• v.31 no.4
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• pp.395-402
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• 2015

This study was considered utilization of hyperspectral image analysis for monitoring. Accordingly we applied to stone cultural properties to data correction methods, image classification techniques, NDVI computation techniques using hyperspectral image. As the results, hyperspectral image analysis was possible making detailed deterioration map, accurate calculation of deterioration rate, mapping of normalized difference vegetation index on the basis of reflectance of each materials. Therefore, hyperspectral image analysis will be used for effective monitoring techniques of stone cultural heritages.