• 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.

• Proceedings of the IEEK Conference
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• 2000.11d
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• pp.77-80
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• 2000

In this paper, we present a new automatic thresholding algorithm based on maximum entropy of two-dimensional pixel histogram. While most of the previous algorithms select thresholds depending only on the histogram of gray level itself in the image, the presented algorithm considers 2D relational histogram of gray levels of two adjacent pixels in the image. Thus, the new algorithm tends to leave salient edge features on the image after thresholding. The experimental results show the good performance of the presented algorithm.

• Journal of the Korean Society for Nondestructive Testing
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• v.12 no.4
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• pp.18-25
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• 1993

This paper is to aim the microdefect evaluation of If package into a quantitative from NDI's image processing of ultrasonic wave. (1) Automatically repeated discrimination analysis method can be devided in the category of all kind of defects on IC package, and also can be possible to have a sampling of partial delamination. (2) It is possible that the information of edge section in silicon chip surrounding can be extractor by the partial image processing of IC package. Also, the crack detection is possible between the resin part and lead frame.

• Communications for Statistical Applications and Methods
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• v.10 no.2
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• pp.325-332
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• 2003

This study was to develop a system for grading apples by their color using statistical image processing. T-test was used to detect edges in apple images and the chain code method was used for contour coding. The histogram and mean gray level of each RGB channel in a ring-shaped region was used to compare apple colors to reference apple color.

• Proceedings of the KIEE Conference
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• 1987.07b
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• pp.1409-1412
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• 1987

Median filter techniques were applied to the chest radiographs for the digital image processing. Histogram distributions were transformed to convert the gray level for detections of the small gray deviations of the image boundaries.

• Proceedings of the KIEE Conference
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• 2006.10c
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• pp.321-323
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• 2006

The light gets darker from center to edge of the light source. Therefore, we can find the center of the sun using shading histogram. Moreover, we can track the exact position of the sun with the shading histogram. In this paper, we propose a new technique using image-processing of digital camera, in order to locate the position of the sun.

• Proceedings of the KIEE Conference
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• 2005.05a
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• pp.63-65
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• 2005

In this study, the automatic face tracking algorithm is proposed by using the color and edge information of a color image. To reduce the effects of variations in the illumination conditions, an acquired CCD color image is first transformed into YCbCr color coordinates, and subsequently the morphological image processing operations, and the elliptical geometric measures are applied to extract the refined facial area.

• Journal of Korea Society of Industrial Information Systems
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• v.3 no.1
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• pp.258-268
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• 1998

This paper presents two sequential advanced split and merge algorithms and a parallel image segmentation algorithm based on them. First, the two advanced methods are obtained from the combination of edge detection and classic split and merge to solve the inherent problems of the classical method. Besides, the parallel image segmentation algorithm on mesh-connected MIMD system considers three types in the merge stage to reduce the communication overhead between processors, such as intraprocessor merge, interprocessor with boundary merge, and interprocessor without boundary merge. Finally , we prove that the proposed algorithms achieve the improved performance by implementing them.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2002.05a
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• pp.1093-1096
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• 2002

In this paper, a new semi-automated cyber character generating method is presented. Local edge detection tools extract face contour from graphic image files. Some graphic manipulation process detailed touch to obtain neat face contour. This method shortens the making process dramatically while maintaining the good quality similar to real face image. Some of the processed images are illustrated for clear explanation.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.29B no.9
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• pp.59-67
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• 1992

Color image enhancement can be achieved easily by using linear form of coordinate system. But some popular color coordinate systems almost have nonlinear characteristics in the geometric form. In this paper, the proposed coordinate system has pseudo-linear form and based on IHS system which represents human color perception appropriately. And for the image intensity processing, an edge-preserving smoothing algorithm is presented.