• International Journal of Advanced Culture Technology
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• v.11 no.1
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• pp.389-394
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• 2023

Based on intelligent visual information and 5G, this paper studies the intelligent visual communication of landscape bamboo buildings, and provides a new method of intelligent perception and interactive computing for the real world, which can represent, model, Perception and cognition; through the integration of virtual and real, the situational understanding of the human-machine-material fusion environment and the interaction with nature. The 5G network can well meet the combination of high-bandwidth uplink transmission and low-latency downlink control. At the same time, 5G-based AR intelligent inspection, remote operation and maintenance guidance, and machine vision inspection. Taking the bamboo building as an example, through field inspections to analyze tourism Bamboo buildings before and after development, and the intelligentization of bamboo buildings based on 5G and visual modeling.

• Journal of the Korea institute for structural maintenance and inspection
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• v.2 no.2
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• pp.151-161
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• 1998

In this study, an attempt is made to apply the concept of fuzzy-bayesian theory to the integrity assessment of RC highway bridge, and uncertainty states are represented in terms of fuzzy sets which define several linguistic variables such as "very good", "good", "average", "poor", "very poor", etc. Especially, the concept of fuzzy conditional probability aids to derive a new reliability analysis which includes the subjective assessment of engineers without introducing any additional correction factors. The fuzzy concept are also used as reliability indexes for the condition assessment based on the proposed models, the proposed fuzzy theory-based approach with the results of visual inspection and extensive field load tests are applied to the integrity assessment of a new RC highway bridge, namely, Jichok bridge.

• Proceedings of the IEEK Conference
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• 2003.07e
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• pp.2275-2278
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• 2003

In this paper, we propose a visual inspection algorithm to detect can print-errors by using multi-camera and image valuing algorithm. The features of the algorithm are to use four cameras that are arranged with 90$^{\circ}$ between each other and to adopt a synthesized image model which represents whole surface of a can. Using the model, detection process is straight forward, namely it is comparing a partial region of the can to a specific region of the model where is previously marked.

• Journal of Ocean Engineering and Technology
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• v.30 no.3
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• pp.194-200
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• 2016

Recently, UUVs (unmanned underwater vehicles) have increasingly been applied in various science and engineering applications. In-water inspection, which used to be performed by human divers, is a potential application for UUVs. In particular, the operational safety and performance of in-water inspection missions can be greatly improved by using an underwater robotic vehicle. The capabilities of hovering maneuvers and automatic image mosaicking are essential for autonomous underwater visual inspection. This paper presents the development of a hover-capable autonomous underwater vehicle system for autonomous in-water inspection, which includes both a hardware platform and operational software algorithms. Some results from an experiment in a model basin are presented to demonstrate the feasibility of the developed system and algorithms.

• International Journal of Highway Engineering
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• v.18 no.6
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• pp.115-122
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• 2016

OBJECTIVES : This study develops an evaluation method, which is useful to inspect pavement condition of specific boroughs. This is because pavement condition is broadly divided into five grades via visual inspection, which does not consider the types of deteriorations, and is decided by an investigator having a subjective viewpoint. This visual inspection method is not a satisfactory method for accurate maintenance when various deteriorations occur. METHODS : The performance model considers several factors such as crack, rutting, and IRI. This method is also modified from borough SPI based on SPI (Seoul Pavement Index). Considering limited budget of borough, PI (prediction index) is suggested, which is related to the grade of pavement condition evaluation and type of materials. Practical correlation review is also conducted with statistical verification by using the Monte Carlo simulation. RESULTS : The results of the study show that modified criteria are reasonable. First, the comparison between the visual inspection result and the SPI result indicates that the R-square value is sufficiently high. Second, through the common section, each evaluation method could be compared, and the result shows considerable similarity, which increases when the range is modified. Finally, PI for predicting remaining life and the random number SPI have common parts, which means that each indicator would be adequate to be used as an evaluation method. CONCLUSIONS : Comparison and analysis results show that the developed evaluation method is reasonable for specific boroughs where financial support is inadequate for the evaluation process by using the newer equipment. Moreover, for more accurate evaluation method, previous visual inspection data should be utilized, and the database of inspection equipment have to be collected.

• Journal of Korean Society for Quality Management
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• v.29 no.3
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• pp.66-78
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• 2001

This paper deals with problem of determining process target value under automated visual inspection(AVI) system. Three independent error sources - digitizing error, illumination error, and positional error - which have a close relationship with the performance of the AVI system, are considered. Assuming that digitizing error is uniformly or normally distributed and illumination and positional errors are normally distributed, respectively, the distribution function for the error of measured lengths is derived when the length of a product is measured by the AVI system. Then, Optimal target values under two error models of AVI system are obtained by minimizing the total expected cost function which consists of give away, rework and penalty cost. To validate two process setting models, AVI system for drinks filling process is made up and test results are discussed.

• Journal of the Korean Society of Safety
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• v.36 no.1
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• pp.18-25
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• 2021

In many developed countries, such as South Korea, efficiently maintaining the aging infrastructures is an important issue. Currently, inspectors visually inspect the infrastructure for maintenance needs, but this method is inefficient due to its high costs, long logistic times, and hazards to the inspectors. Thus, in this paper, a novel crack inspection approach for concrete bridges is proposed using integrated image processing techniques. The proposed approach consists of four steps: (1) training a deep learning model to automatically detect cracks on concrete bridges, (2) acquiring in-situ images using a drone, (3) generating orthomosaic images based on 3D modeling, and (4) detecting cracks on the orthmosaic image using the trained deep learning model. Cascade Mask R-CNN, a state-of-the-art instance segmentation deep learning model, was trained with 3235 crack images that included 2415 hard negative images. We selected the Tancheon overpass, located in Seoul, South Korea, as a testbed for the proposed approach, and we captured images of pier 34-37 and slab 34-36 using a commercial drone. Agisoft Metashape was utilized as a 3D model generation program to generate an orthomosaic of the captured images. We applied the proposed approach to four orthomosaic images that displayed the front, back, left, and right sides of pier 37. Using pixel-level precision referencing visual inspection of the captured images, we evaluated the trained Cascade Mask R-CNN's crack detection performance. At the coping of the front side of pier 37, the model obtained its best precision: 94.34%. It achieved an average precision of 72.93% for the orthomosaics of the four sides of the pier. The test results show that this proposed approach for crack detection can be a suitable alternative to the conventional visual inspection method.

• Journal of the Korean Society of Clothing and Textiles
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• v.36 no.6
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• pp.631-640
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• 2012

This research project investigates the differences of various body locations (between 3D body models) and drapes garments digitally onto 3D body models. Three different subject models will be given explication. It consisted of (1) data collection of three-dimensional scans (2) creation of 3D body representations (3) comparison of avatar shapes and measurements (4) visualization and assessment of 3D body models and their 3D virtual garments. The study tests a theory of impact by differences in avatars by pattern design. A visual inspection of avatars showed clear differences between the six avatar types (in the generating process); however, there was notably less difference between 3D garment simulations based upon the six avatars produced. This demonstrated that there was less influence on the 3D garments than was predicted after a visual inspection of the avatars.

• Journal of Korean Association for Spatial Structures
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• v.22 no.2
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• pp.19-27
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• 2022

Visual inspection methods have limitations, such as reflecting the subjective opinions of workers. Moreover, additional equipment is required when inspecting the high-rise buildings because the height is limited during the inspection. Various methods have been studied to detect concrete cracks due to the disadvantage of existing visual inspection. In this study, a crack detection technology was proposed, and the technology was objectively and accurately through AI. In this study, an efficient method was proposed that automatically detects concrete cracks by using a Convolutional Neural Network(CNN) with the Orthomosaic image, modeled with the help of UAV. The concrete cracks were predicted by three different CNN models: AlexNet, ResNet50, and ResNeXt. The models were verified by accuracy, recall, and F1 Score. The ResNeXt model had the high performance among the three models. Also, this study confirmed the reliability of the model designed by applying it to the experiment.

• Journal of the Korea institute for structural maintenance and inspection
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• v.27 no.5
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• pp.30-39
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• 2023

Large dams, which are critical infrastructures for disaster prevention, are exposed to various risks such as aging, floods, and earthquakes. Better dam safety inspection and diagnosis using digital transformation technologies are needed. Traditional visual inspection methods by human inspectors have several limitations, including many inaccessible areas, danger of working at heights, and know-how based subjective inspections. In this study, drone photogrammetry was performed on two large dams to evaluate the applicability of digital data-based dam safety inspection and propose a data management methodology for continuous use. High-quality 3D digital models with GSD (ground sampling distance) within 2.5 cm/pixel were generated by flat double grid missions and manual photography methods, despite reservoir water surface and electromagnetic interferences, and severe altitude differences ranging from 42 m to 99.9 m of dam heights. Geometry profiles of the as-built conditions were easily extracted from the generated 3D mesh models, orthomosaic images, and digital surface models. The effectiveness of monitoring dam deformation by photogrammetry was confirmed. Cracks and deterioration of dam concrete structures, such as spillways and intake towers, were detected and visualized efficiently using the digital 3D models. This can be used for safe inspection of inaccessible areas and avoiding risky tasks at heights. Furthermore, a methodology for mapping the inspection result onto the 3D digital model and structuring a relational database for managing deterioration information history was proposed. As a result of measuring the labor and time required for safety inspection at the SYG Dam spillway, the drone photogrammetry method was found to have a 48% productivity improvement effect compared to the conventional manpower visual inspection method. The drone photogrammetry-based dam safety inspection is considered very effective in improving work productivity and data reliability.