• Journal of Korean Tunnelling and Underground Space Association
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• v.26 no.4
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• pp.365-384
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• 2024

The development of scanning technology is accelerating for safer and more efficient automated inspection than human-based inspection. Research on automatically detecting facility damage from images collected using computer vision technology is also increasing. The pixel size, quality, and quantity of an image can affect the performance of deep learning or image processing for automatic damage detection. This study is a basic to acquire high-quality raw image data and camera performance of a mobile tunnel scanning system for automatic detection of damage based on deep learning, and proposes a method to quantitatively evaluate image quality. A test chart was attached to a panel device capable of simulating a moving speed of 40 km/h, and an indoor test was performed using the international standard ISO 12233 method. Existing image quality evaluation methods were applied to evaluate the quality of images obtained in indoor experiments. It was determined that the shutter speed of the camera is closely related to the motion blur that occurs in the image. Modulation transfer function (MTF), one of the image quality evaluation method, can objectively evaluate image quality and was judged to be consistent with visual observation.

• Smart Structures and Systems
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• v.24 no.5
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• pp.567-585
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• 2019

In the past two decades, structural health monitoring (SHM) systems have been widely installed on various civil infrastructures for the tracking of the state of their structural health and the detection of structural damage or abnormality, through long-term monitoring of environmental conditions as well as structural loadings and responses. In an SHM system, there are plenty of sensors to acquire a huge number of monitoring data, which can factually reflect the in-service condition of the target structure. In order to bridge the gap between SHM and structural maintenance and management (SMM), it is necessary to employ advanced data processing methods to convert the original multi-source heterogeneous field monitoring data into different types of specific physical indicators in order to make effective decisions regarding inspection, maintenance and management. Conventional approaches to data analysis are confronted with challenges from environmental noise, the volume of measurement data, the complexity of computation, etc., and they severely constrain the pervasive application of SHM technology. In recent years, with the rapid progress of computing hardware and image acquisition equipment, the deep learning-based data processing approach offers a new channel for excavating the massive data from an SHM system, towards autonomous, accurate and robust processing of the monitoring data. Many researchers from the SHM community have made efforts to explore the applications of deep learning-based approaches for structural damage detection and structural condition assessment. This paper gives a review on the deep learning-based SHM of civil infrastructures with the main content, including a brief summary of the history of the development of deep learning, the applications of deep learning-based data processing approaches in the SHM of many kinds of civil infrastructures, and the key challenges and future trends of the strategy of deep learning-based SHM.

• The Journal of the Korea Contents Association
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• v.17 no.11
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• pp.303-311
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• 2017

Safety accidents in railway work continue to increase every year; Engineer's negligence, trackside worker's sensory deprivation and signalman's mistake are the main reasons of such incidents. We consider this problem by far the most urgent matter in railway work because of its steady increase and risk of taking a person's life. Based on that account, a new alarm system has developed, that is called Railway Access Alarm System, to allow railway workers to sense the access of trains with not only vision, but also hearing. The detector device of this system is installed on both sides of the track locating 1.5km from the workplace. When the train enters the place, the detector device can sense the entering, sending the detect sign of train to the alarm unit, then the alarm unit warns the workers by the LED lighting and sirens. This system has several advantages compared to previous systems. First, it recognizes the train at a long distance. Secondly, there is no need for wiring work since it is a wireless system. At last, the system works by rechargeable batteries and solar charger so that it is installed in the work places where there is no external power supply. Moreover, it is proven that the system is 100% reliable by the successful on-the spot inspection evaluating the capability.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.36S no.11
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• pp.79-88
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• 1999

In this paper we address the tracking method which tracks only target object in image sequence including moving object. We use a contour tracking algorithm based on intensity and motion boundaries. The motion of the moving object contour in the image is assumed to be well describable by an affine motion model with a translation, a change in scale and a rotation. The moving object contour is represented by B-spline, the position and motion of which is estimated along the image sequence. we use pattern recognition to identify target object. In order to use linear Kalman Filters we decompose the estimation process into two filters. One is estimating the affine motion parameters and the other the shape of moving object contour. In some experiments with dial plate we show that this method enables us to obtain the robust motion estimates and tracking trajectories even in case of including obstructive object.

• Journal of Korea Multimedia Society
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• v.9 no.9
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• pp.1131-1138
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• 2006

This paper proposes a novel method for grading of underground communication conduits by laser projection image analysis. The equipment thrust into conduit consists of a laser diode, a light emitting diode and a camera, the laser diode is utilized for generating projection image onto pipe wall, the light emitting diode for lighting environment and the image of conduit is acquired by the camera. In order to segment profile region, we used a novel color difference model and multiple thresholds method. The shape of profile ring is represented as a minimum diameter and the Fourier descriptor, and then the pipe status is graded by the rule-based method. Both local and global features of the segmented ring shaped, the minimum diameter and the Fourier descriptor, are utilized, therefore injured and distorted pipes can be correctly graded. From the experimental results, the classification is measured with accuracy such that false alarms are less than 2% under the various conditions.

• Journal of Biosystems Engineering
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• v.19 no.2
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• pp.124-137
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• 1994

Visual features of a mushroom(Lentinus Edodes, L.) are critical in grading and sorting as most agricultural products are. Because of its complex and various visual features, grading and sorting of mushrooms have been done manually by the human expert. To realize the automatic handling and grading of mushrooms in real time, the computer vision system should be utilized and the efficient and robust processing of the camera captured visual information be provided. Since visual features of a mushroom are distributed over the front and back sides, recognizing sides and states of the stalk including the stalk orientation from the captured image is a prime process in the automatic task processing. In this paper, the efficient and robust recognition process identifying the front and back side and the state of the stalk was developed and its performance was compared with other recognition trials. First, recognition was tried based on the rule set up with some experimental heuristics using the quantitative features such as geometry and texture extracted from the segmented mushroom image. And the neural net based learning recognition was done without extracting quantitative features. For network inputs the segmented binary image obtained from the combined type automatic thresholding was tested first. And then the gray valued raw camera image was directly utilized. The state of the stalk seriously affects the measured size of the mushroom cap. When its effect is serious, the stalk should be excluded in mushroom cap sizing. In this paper, the stalk removal process followed by the boundary regeneration of the cap image was also presented. The neural net based gray valued raw image processing showed the successful results for our recognition task. The developed technology through this research may open the new way of the quality inspection and sorting especially for the agricultural products whose visual features are fuzzy and not uniquely defined.

• Journal of Internet Computing and Services
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• v.23 no.3
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• pp.35-45
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• 2022

Even in recent years, treatment of first-aid patients is still often delayed due to a shortage of medical resources in marginalized areas. Research on automating the analysis of medical data to solve the problems of inaccessibility for medical services and shortage of medical personnel is ongoing. Computer vision-based medical inspection automation requires a lot of cost in data collection and labeling for training purposes. These problems stand out in the works of classifying lesion that are rare, or pathological features and pathogenesis that are difficult to clearly define visually. Anomaly detection is attracting as a method that can significantly reduce the cost of data collection by adopting an unsupervised learning strategy. In this paper, we propose methods for detecting abnormal images on chest X-RAY images as follows based on existing anomaly detection techniques. (1) Normalize the brightness range of medical images resampled as optimal resolution. (2) Some feature vectors with high representative power are selected in set of patch features extracted as intermediate-level from lesion-free images. (3) Measure the difference from the feature vectors of lesion-free data selected based on the nearest neighbor search algorithm. The proposed system can simultaneously perform anomaly classification and localization for each image. In this paper, the anomaly detection performance of the proposed system for chest X-RAY images of PA projection is measured and presented by detailed conditions. We demonstrate effect of anomaly detection for medical images by showing 0.705 classification AUROC for random subset extracted from the PadChest dataset. The proposed system can be usefully used to improve the clinical diagnosis workflow of medical institutions, and can effectively support early diagnosis in medically poor area.

• Journal of the Korean Institute of Intelligent Systems
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• v.24 no.3
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• pp.271-278
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• 2014

In spite of that stop line and crosswalk should be aware of the most basic objects in transportation system, its features extracted are very limited. In addition to image-based recognition technology, laser and RF, GPS/INS recognition technology, it is difficult to recognize. For this reason, the limited research in this area has been done. In this paper, the algorithm to recognize the stop line and crosswalk is designed and implemented using image-based recognition technology with the images input through a vision sensor. This algorithm consists of three functions.; One is to select the area, in advance, needed for feature extraction in order to speed up the data processing, 'Region of Interest', another is to process the images only that white color is detected more than a certain proportion in order to remove the unnecessary operation, 'Color Pattern Inspection', the other is 'Feature Extraction and Recognition', which is to extract the edge features and compare this to the previously-modeled one to identify the stop line and crosswalk. For this, especially by using case based feature comparison algorithm, it can identify either both stop line and crosswalk exist or just one exists. Also the proposed algorithm is to develop existing researches by comparing and analysing effect of in-vehicle camera installation and changes in recognition rate of distance estimation and various constraints such as backlight and shadow.

• Explosives and Blasting
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• v.22 no.3
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• pp.57-64
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• 2004

There are two major types of underwater blasting at Korea, bridges and harbor construction work. Pier blasting for lay the foundation bridges construction is used dry excavation working (drilling and charging) after pump out water and then fire pump in water that is same as bench blasting. In contrast, underwater blasting for harbor construction and increase of harbor load depth is used to barge with digging equipment that is in oder to drilling on the surface and blasting work(charge, hook-up) under water. Thus, there are need to special concern such as charge method and hook-up method different from tunnel blasting work and bench blasting work. If do not use special concern breaks out dead pressure and mis fire because of there are so many difficult condition such as water pressure, obstruct field of vision. In this study underwater blasting at Busan Harbor Construction have consider with special concern that is plastic pipe charge method used to MegaMITE I and specialized buoy hook- up method make far initial system detonate on the surface used to TLD. The results is designed blast pattern charge per delay effect an inspection of verify between predict velocity and measure velocity. minimized break out mis fire consideration charge method, hook up method. According to result best underwater blasting design is 105mm drilling dia, MeGAMITE II, HiNLL Plus(non electric detonator).