• Proceedings of the Korean Society of Precision Engineering Conference
• /
• 2003.06a
• /
• pp.946-950
• /
• 2003

In this paper, a computer-vision based pipe shape inspection algorithm is developed. The algorithm uses the modified Hough transformation and a line-scanning approach to identify the edge line and radius of the pipe image, from which the eccentricity and dimension of the pipe-end is calculated. Line and circle detection was performed using Laplace operator with input image, which are acquired from the front and side cameras. In order to minimize the memory usage and the processing time, a clustering method with the modified Hough transformation for line detection. The dimension of inner and outer radius of pipe is calculated by proposed line-scanning method. The method scans several lines along the X and Y axes, calculating the eccentricity of inner and outer circle. by which pipes with wrong end-shape can be classified removed.

• Proceedings of the Korean Society of Precision Engineering Conference
• /
• 2002.10a
• /
• pp.521-525
• /
• 2002

In this paper, a computer-vision based pipe-inspection algorithm is developed. The algorithm uses the modified Hough transformation and a line-scanning approach to identify the edge line and radius of the pipe image, from which the eccentricity and dimension of the pipe-end is calculated. Line and circle detection was performed using Laplace operator with input image, which are acquired from the front and side cameras. In order to minimize the memory usage and the processing time, a clustering method with the modified Hough transformation for line detection. The dimension of inner and outer radius of pipe is calculated by proposed line-scanning method. The method scans several lines along the X and Y axes, calculating the eccentricity of inner and outer circle, by which pipes with wrong end-shape can be classified removed.

• Proceedings of the IEEK Conference
• /
• 2000.06d
• /
• pp.139-142
• /
• 2000

$\mu$BGA(Ball Grid Array) is growing in response to a great demand for smaller and lighter packages for the use in laptop, mobile phones and other evolving products. However it is not easy to find its defect by human visual due to in very small dimension. From this point of view, we are interested its development of a vision based automated inspection algorithm. For this, first a 2D view of $\mu$BGA is described under a special blue illumination. Second, a notation-invariant 2D inspection algorithm is developed. Finally a 3D inspection algorithm is proposed for the case of stereo vision system. As a simulation result, it is shown that 3D defect not easy to find by 2D algorithm can be detected by the proposed inspection algorithm.

• Proceedings of the IEEK Conference
• /
• 2003.07e
• /
• pp.2335-2338
• /
• 2003

This paper presents a real-time PCB (Printed Circuit Board) vision inspection system. This system can detect the OPEN and SHORT of the PCB which of the line width is 150$\mu\textrm{m}$. Our PCB inspection system is based on the referential method. Since the size of the captured PCB image is very large, the image is divided into 512${\times}$512 images to apply the accurate alignment efficiently. To correct the misalignment between the reference image and the inspection image, pattern matching is performed. In order to implement the proposed algorithm in real-time, we use the SIMD instruction and the double buffering structures. Our experiential results show the effectiveness of the developed inspection algorithm.

• International journal of advanced smart convergence
• /
• v.8 no.3
• /
• pp.39-45
• /
• 2019

This paper presents an optical technique for the three-dimensional (3D) shape inspection of micro solder balls used in ball-grid array (BGA) packaging. The proposed technique uses an optical source composed of spatially arranged light-emitting diodes (LEDs) and the results are derived based on the specular reflection characteristics of the micro solder balls for BGA A vision system comprising a camera and LEDs is designed to capture the reflected images of multiple solder balls arranged arbitrarily on a tray and the locations of the LED point-light-source reflections in each ball are determined via image processing, for shape inspection. The proposed methodology aims to determine the presence of defects in 3D BGA shape using the statistical information of the relative positions of multiple BGA balls, which are included in the image. The presence of the BGA balls with large deviations in relative position imply the inconsistencies in their shape. Experiments were conducted to verify that the proposed method could be applied to inspection without sophisticated mechanism and productivity problem.

• Computers and Concrete
• /
• v.33 no.4
• /
• pp.341-348
• /
• 2024

Ultra-high-performance concrete (UHPC) has received remarkable attentions in civil infrastructure due to its unique mechanical characteristics and durability. UHPC gains increasingly dominant in essential structural elements, while its unique properties pose challenges for traditional inspection methods, as damage may not always manifest visibly on the surface. As such, the need for robust inspection techniques for detecting cracks in UHPC members has become imperative as traditional methods often fall short in providing comprehensive and timely evaluations. In the era of artificial intelligence, computer vision has gained considerable interest as a powerful tool to enhance infrastructure condition assessment with image and video data collected from sensors, cameras, and unmanned aerial vehicles. This paper presents a computer vision-based approach employing deep learning to detect cracks in UHPC beams, with the aim of addressing the inherent limitations of traditional inspection methods. This work leverages computer vision to discern intricate patterns and anomalies. Particularly, a convolutional neural network architecture employing transfer learning is adopted to identify the presence of cracks in the beams. The proposed approach is evaluated with image data collected from full-scale experiments conducted on UHPC beams subjected to flexural and shear loadings. The results of this study indicate the applicability of computer vision and deep learning as intelligent methods to detect major and minor cracks and recognize various damage mechanisms in UHPC members with better efficiency compared to conventional monitoring methods. Findings from this work pave the way for the development of autonomous infrastructure health monitoring and condition assessment, ensuring early detection in response to evolving structural challenges. By leveraging computer vision, this paper contributes to usher in a new era of effectiveness in autonomous crack detection, enhancing the resilience and sustainability of UHPC civil infrastructure.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2010.05a
• /
• pp.569-571
• /
• 2010

In this paper, we develop a vision inspection system for inspecting flaws on plastic products such as insufficient moldings, spots, scratches. The inspection algorithm for this system consist of image binarization for curved structure of plastic products, image noise removal using morphology operation, labeling methods for candidate regions and image filtering and calibration method for flaw inspection. In order to improve its performance, we also develop fast image processing algorithm based on GUI. To verify the effectiveness of this system, we conducted evaluation for the system accuracy and the inspection algorithm processing time.

• Journal of the Korean Society for Precision Engineering
• /
• v.22 no.11 s.176
• /
• pp.82-90
• /
• 2005

Recently, the in-line vision inspection has become the subject of growing research area in the visual control systems and robotic intelligent fields that are required exact three-dimensional pose. The objective of this article is to study the pc based in line visual inspection with the hand-eye structure. This paper suggests three dimensional structured light measuring principle and design method of laser sensor header. The hand-eye laser sensor have been studied for a long time. However, it is very difficult to perform kinematical analysis between laser sensor and robot because the complicated mathematical process are needed for the real environments. In this problem, this paper will propose auto-calibration concept. The detail process of this methodology will be described. A new thinning algorithm and constrained hough transform method is also explained in this paper. Consequently, the developed in-line inspection module demonstrate the successful operation with hole, gap, width or V edge.

• Proceedings of the KIEE Conference
• /
• 2006.10c
• /
• pp.425-427
• /
• 2006

In this paper, an automatic side-view inspection algorithm for LCD modules is proposed. Until now, most parts of inspection is performed by human inspectors, which means very high product costs. So inspection automation is the very hot issue in the LCD industries. However, it is not easy problem to replace the human by computer vision system. In the many inspections which are based on the human eyes, side-view inspection is most hard problem to solve. In this paper, an image morphing algorithm is developed, which will help to enable the automation of the side-view inspection process.

• Corrosion Science and Technology
• /
• v.18 no.6
• /
• pp.253-257
• /
• 2019

A deep learning based real-time painting surface inspection algorithm is proposed herein, designed for developing an autonomous inspection drone. The painting surface inspection is usually conducted manually. However, the manual inspection has a limitation in obtaining accurate data for correct judgement on the surface because of human error and deviation of individual inspection experiences. The best method to replace manual surface inspection is the vision-based inspection method with a camera, using various image processing algorithms. Nevertheless, the visual inspection is difficult to apply to surface inspection due to diverse appearances of material, hue, and lightning effects. To overcome technical limitations, a deep learning-based pattern recognition algorithm is proposed, which is specialized for painting surface inspections. The proposed algorithm functions in real time on the embedded board mounted on an autonomous inspection drone. The inspection results data are stored in the database and used for training the deep learning algorithm to improve performance. The various experiments for pre-inspection of painting processes are performed to verify real-time performance of the proposed deep learning algorithm.