• International conference on construction engineering and project management
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• 2024.07a
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• pp.311-318
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• 2024

Quality control in construction projects necessitates the detection of defects during construction. Currently, this task is performed manually by site supervisors. This manual process is inefficient, labor-intensive, and prone to human error, potentially leading to decreased productivity. To address this issue, research has been conducted to automate defect detection using computer vision-based object detection technologies. However, these studies often suffer from a lack of data for training deep learning models, resulting in inadequate accuracy. This study proposes a method to improve the accuracy of deep learning models through the use of virtual image data. The target building is created as a 3D model and finished with materials similar to actual components. Subsequently, a virtual defect texture is produced by layering three types of images: defect information, area information, and material information images, to fabricate materials with defects. Images are generated by rendering the 3D model and the defect, and annotations are created for segmentation. This approach creates a hybrid dataset by combining virtual data with actual site image data, which is then used to train the deep learning model. This research was conducted on the tile process of finishing construction projects, focusing on cracks and falls as the target defects. The training results of the deep learning model show that the F1-Score increased by 12.08% for falls and cracks when using the hybrid dataset compared to the real image dataset alone, validating the hybrid data approach. This study contributes not only to unmanned and automated smart construction management but also to enhancing safety on construction sites. To establish an integrated smart quality management system, it is necessary to detect various defects simultaneously with high accuracy. Utilizing this method for automatic defect detection in other types of construction can potentially expand the possibilities for implementing an integrated smart quality management system.

• Journal of the Korean Society for Precision Engineering
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• v.32 no.11
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• pp.989-995
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• 2015

In the literature, various stochastic anomaly detection methods, such as limit checking and PCA-based approaches, have been applied to weld defect detection. However, it is still a challenge to identify meaningful defect patterns from very limited sensor signals of laser welding, characterized by intermittent, discontinuous, very short, and non-stationary random signals. In order to effectively analyze the physical characteristics of laser weld signals: plasma intensity, weld pool temperature, and back reflection, we first transform the raw data of laser weld signals into the form of event logs. This is done by multidimensional discretization and event-codification, after which the event logs are decoded to extract weld defect patterns by $Na{\ddot{i}}ve$ Bayes classifier. The performance of the proposed method is examined in comparison with the commercial solution of PRECITEC's LWM$^{TM}$ and the most recent PCA-based detection method. The results show higher performance of the proposed method in terms of sensitivity (1.00) and specificity (0.98).

• Journal of the Korea Institute of Information and Communication Engineering
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• v.17 no.9
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• pp.2145-2152
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• 2013

Paper manufacturing industries have huge facilities with automatic equipments. Especially, in order to improve the efficiency of the paper manufacturing processes, it is necessary to detect the paper cutting defect effectively and to classify the causes correctly. In this paper, we review the problems of web monitoring system and web inspection system that have been traditionally used in industries for defect detection. Then we propose a novel paper cutting defect detection method based on the local binary pattern analysis and its implementation to mitigate the practical problems in industry environment. The proposed algorithm classifies the defects into edge-type and region-type and then it is shown that the proposed system works stably on the real paper cutting defect detection system.

• Journal of Information Processing Systems
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• v.16 no.5
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• pp.1008-1014
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• 2020

Aiming at the defect detection quality of denim fabric, this paper designs an improved algorithm based on the optimized Gabor filter. Firstly, we propose an improved defect detection algorithm of jean fabric based on the maximum two-dimensional image entropy and the loss evaluation function. Secondly, 24 Gabor filter banks with 4 scales and 6 directions are created and the optimal filter is selected from the filter banks by the one-dimensional image entropy algorithm and the two-dimensional image entropy algorithm respectively. Thirdly, these two optimized Gabor filters are compared to realize the common defect detection of denim fabric, such as normal texture, miss of weft, hole and oil stain. The results show that the improved algorithm has better detection effect on common defects of denim fabrics and the average detection rate is more than 91.25%.

• Journal of the Korean Society for Precision Engineering
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• v.15 no.7
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• pp.149-159
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• 1998

The importance of soundness and safety evaluation in weld zone using by the ultrasonic wave has been recently increased rapidly because of the collapses of huge structures and safety accidents. Especially, the ultrasonic method that has been often used for a major non-destructive testing(NDT) technique in many engineering fields plays an important role as a volume test method. Hence, the defecting any defects of weld Bone in austenitic stainless steel type 304 using by ultrasonic wave and neural network is explored in this paper. In order to detect defects, a distance amplitude curve on standard scan sensitivity and preliminary scan sensitivity represented of the relation between ultrasonic probe, instrument, and materials was drawn based on a quantitative standard. Also, a total of 93％ of defect types by testing 30 defect patterns after organizing neural network system, which is learned with an accuracy of 99％, based on ultrasonic evaluation is distinguished in order to classify defects such as holes or notches in experimental results. Thus, the proposed ultrasonic wave and neural network is useful for defect detection and Ultrasonic Non-Destructive Evaluation(UNDE) of weld zone in austenitic stainless steel 304.

• Journal of Magnetics
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• v.21 no.3
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• pp.437-441
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• 2016

Dipole model based analytical expression is proposed to estimate the length and depth of the rectangular defect on ferromagnetic pipe. Among the three leakage profiles of Magnetic Flux Leakage (MFL), radial and axial leakage profiles are considered in this work. Permeability variation of the specimen is ignored by considering the flux density as close to saturation level of the inspected specimen. Comparing the profile of both the components, radial leakage profile furnishes the better estimation of defect parameter. This is evident from the results of error percentage of length and depth of the defect. Normalized pattern of the proposed analytical model radial leakage profile is good agreement with the experimentally obtained profile support the performance of proposed expression.

• Smart Structures and Systems
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• v.11 no.3
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• pp.299-314
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• 2013

This paper presents a nonlinear time series analysis technique for evaluating machine defect severity, based on the Recurrence Plot (RP) entropy. The RP entropy is calculated from the probability distribution of the diagonal line length in the recurrence plot, which graphically depicts a system's dynamics and provides a global picture of the autocorrelation in a time series over all available time-scales. Results of experimental studies conducted on a spindle-bearing test bed have demonstrated that, as the working condition of the bearing deteriorates due to the initiation and/or progression of structural damages, the frequency information contained in the vibration signal becomes increasingly complex, leading to the increase of the RP entropy. As a result, RP entropy can serve as an effective indicator for defect severity assessment of rolling bearings.

• Journal of the Korean Society for Nondestructive Testing
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• v.22 no.6
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• pp.637-642
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• 2002

An arc-shaped line array slit has been used for the laser generation of focused Lamb waves. The spatially expanded Nd:YAG pulse laser was illuminated through the arc-shaped line array slit on the surface of a sample plate to generate the Lamb waves of the same pattern as the slit. Then the generated Lamb waves were focused at the focal point of which distance from the slit position is dependent on the curvature of slit arc. The proposed method showed better spatial resolution than the conventional linear array slit in the detection of laser machined linear defect and drill machined circular defect on aluminum plates of 2mm thickness. Using the focused waves, we could detect the linear defect and the circular defect with the improvement of spatial resolution. The method can also be combined with the scanning mechanism to get an image just like by the scanning acoustic microscope(SAM).

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2005.06a
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• pp.946-949
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• 2005

This paper deals with the algorithm development that inspects defects such as Bright Defect Dots, Dark Defect Dots, and Line Defect caused by the process of LCD(Liquid Crystal Display). While most of LCD production process is automated, the inspection of LCD panel and its appearance depends on manual process. So, the quality of the inspection is affected by the condition of worker. Especially, the more LCD size increases, the more the worker feels fatigued, which causes the probability of miss judgement. So, the automated inspection is required to manage the consistent quality of the product and reduce the production costs. In this paper, to solve these problems, we developed the imaging processing algorithm to inspect the defects in captured image of LCD. Experimental results reveal that we can recognize various types of defect of LCD with good accuracy and high speed.

• Journal of Information Processing Systems
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• v.19 no.6
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• pp.745-755
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• 2023

The wire rope is an indispensable production machinery in coal mines. It is the main force-bearing equipment of the underground traction system. Accurate detection of wire rope defects and positions exerts an exceedingly crucial role in safe production. The existing defect detection solutions exhibit some deficiencies pertaining to the flexibility, accuracy and real-time performance of wire rope defect detection. To solve the aforementioned problems, this study utilizes the camera to sample the wire rope before the well entry, and proposes an object based on YOLOv5. The surface small-defect detection model realizes the accurate detection of small defects outside the wire rope. The transfer learning method is also introduced to enhance the model accuracy of small sample training. Herein, the enhanced YOLOv5 algorithm effectively enhances the accuracy of target detection and solves the defect detection problem of wire rope utilized in mine, and somewhat avoids accidents occasioned by wire rope damage. After a large number of experiments, it is revealed that in the task of wire rope defect detection, the average correctness rate and the average accuracy rate of the model are significantly enhanced with those before the modification, and that the detection speed can be maintained at a real-time level.