컴퓨터용 Lead Screw의 자동검사 시스템 개발

PurposeHeritage is the latent part of a sustainable built environment. Conservation and preservation of heritage is one of the United Nations' (UN) sustainable development goals. Many social and natural factors seriously threaten heritage structures by deteriorating and damaging the original. Therefore, regular visual inspection of heritage structures is necessary for their conservation and preservation. Conventional inspection practice relies on manual inspection, which takes more time and human resources. The inspection system seeks an innovative approach that should be cheaper, faster, safer and less prone to human error than manual inspection. Therefore, this study aims to develop an automatic system of visual inspection for the built heritage.Design/methodology/approachThe artificial intelligence-based automatic defect detection system is developed using the faster R-CNN (faster region-based convolutional neural network) model of object detection to build an automatic visual inspection system. From the English and Dutch cemeteries of Surat (India), images of heritage structures were captured by digital camera to prepare the image data set. This image data set was used for training, validation and testing to develop the automatic defect detection model. While validating this model, its optimum detection accuracy is recorded as 91.58% to detect three types of defects: “spalling,” “exposed bricks” and “cracks.”FindingsThis study develops the model of automatic web-based visual inspection systems for the heritage structures using the faster R-CNN. Then it demonstrates detection of defects of spalling, exposed bricks and cracks existing in the heritage structures. Comparison of conventional (manual) and developed automatic inspection systems reveals that the developed automatic system requires less time and staff. Therefore, the routine inspection can be faster, cheaper, safer and more accurate than the conventional inspection method.Practical implicationsThe study presented here can improve inspecting the built heritages by reducing inspection time and cost, eliminating chances of human errors and accidents and having accurate and consistent information. This study attempts to ensure the sustainability of the built heritage.Originality/valueFor ensuring the sustainability of built heritage, this study presents the artificial intelligence-based methodology for the development of an automatic visual inspection system. The automatic web-based visual inspection system for the built heritage has not been reported in previous studies so far.

Electronic Toll Collection (ETC) system is an important part of modern intelligent transport system, which plays an important role in intelligent car parks and congestion charging. Electronic Toll Collection (ETC) system includes automatic vehicle identification system and electronic billing system. The automatic vehicle identification system includes three parts: automatic vehicle identification (AVI), automatic vehicle model identification (AVC) and inspection system (ES).The application of ETC in intelligent car parks and congestion charging also includes automatic vehicle identification system (automatic vehicle identification, automatic vehicle model identification and inspection system) and electronic billing system. In intelligent car parks, ETC technology achieves fast and accurate automatic toll payment by automatically identifying the ETC tags carried by vehicles, which greatly improves the efficiency of car parks and reduces the queuing and waiting time due to manual toll collection. In terms of congestion charging, ETC technology provides an effective means to implement congestion charge collection. By setting up ETC toll lanes in core urban areas or busy road sections, the system can automatically record the information of the vehicles passing through and carry out toll settlement, thus realising dynamic toll collection according to the degree of congestion and the frequency of vehicle use. This congestion charging strategy aims to regulate urban traffic flow through economic means, alleviate traffic congestion and promote the sustainable development of urban transport. Although China's theoretical research on congestion charging, has been relatively mature, but affected by a variety of factors, no city has yet to start the implementation of congestion charging, the lack of experience in practice. Singapore will not stop the car charging system (ETC) technology successfully applied to urban congestion charging and intelligent car parks, ETC system in intelligent car parks and congestion charging application, help to promote the development of urban transport to a more intelligent, green direction. China can learn from the successful experience of foreign countries and apply it to its own intelligent car parks and congestion charging. This paper introduces the basic principle of ETC, discusses the research progress of ETC in terms of technical characteristics, and analyses the application scenarios of ETC in intelligent car parks and congestion charging. By examining the opportunities offered by Electronic Toll Collection (ETC) systems in smart car parks and congestion pricing, this study aims to delve into the transformative impacts of Electronic Toll Collection (ETC) systems on intelligent mobility and contribute to the sustainable development of intelligent mobility.

Manual visual inspection is the standard method to evaluate cleanliness, scratches, and digs on optical components according to ISO 10110-7 or MIL-PRF-13830B. However, the limited optical resolution of the human eye turns the inspection of critical samples like high power optics or sensor cover glasses into a time-consuming and tiring task with inherently subjective results. Machine vision systems provide an objective alternative with reliable and reproducible results and additional advantages: Documentation in the form of inspection reports can be generated at no extra cost and data can be collected to analyze typical problems in the manufacturing process. Automated inspection systems need high resolution and well-designed illumination for the inspected part to ensure visibility of smallest imperfections with sizes as small as few micrometers. At the same time, short duty cycles of the inspection system are a key user requirement. Manual-visual is performed in dark field conditions. In automated inspection systems, a dark field optical setup is also advantageous, giving the best imperfection contrast. Generating good dark field conditions when inspecting a curved optical, i.e. specular, surface is not trivial. Using a multitude of illumination angles is necessary for good (and orientation-independent) imperfection visibility, but every additional illumination direction increases the risk of specular reflections into the camera, leading to blind spots in the images. We have developed two approaches to deal with this problem: In the first, reflections are avoided by design, in the second, reflections are treated in data processing. Our commercially available ARGOS system is using a line-scan camera in combination with a rotation stage. The light source is illuminating from almost every direction, except for the direction parallel to the line sensor of the camera itself. This gives excellent imperfection visibility while avoiding direct reflections and maintaining dark-field conditions on rotationally symmetric samples with almost arbitrary curvature. The second approach that has been successfully implemented is suitable also for matrix cameras: Multiple illumination configurations are used sequentially, each generating direct reflections in different portions of the image. These image-sets are then combined into a dark-field image without reflections, by applying suitable filter masks to remove specular reflections from the images. The ARGOS-approach with the line-scan camera has the additional advantage, that very high image resolution is possible by rotating the sample while observing a line aligned with the sample radius. In the standard version, image resolution is up to 250 mega pixels; in the high-resolution version up to 1 giga pixel is possible – in scan times of a few seconds. On the other hand, the requirement to rotate the sample is not ideal for an automated inspection of a batch of samples. In cases when the large resolution is not required, e.g. for smaller elements, using a matrix camera is more efficient, as the acquisition of a sufficient number of images with different illumination conditions takes only a fraction of a second, allowing for short overall inspection times.

The perfect Printed Circuit Board (PCB) plays a very important role in every electronic device as well as in automation systems. So, it is very important to find defects in the PCB before installing it to any system or any device. However, PCB Manufacturers use various inspection systems in the process of manufacturing PCBs for detecting various types of defects in the PCB. In this article, we present the Automated assembled PCB Inspection System. This system finds defects such as missing components and improper position of its components by using the Pattern matching Technique where a good known score of template image is matched with the score of the test image. This system gives results at each inspection within 10 Seconds and the result given by this system are passed or fail in the form of an array sheet. This automated inspection system is created by using NI Vision Builder AI and NI LabVIEW technology. Ni Vision Builder AI has been used to create the algorithm. And NI LabVIEW has been used to create the application.

Vehicle inspection at ports of entry is a critical component of border security. One part of the vehicle screening process involves customs and border protection (CBP) personnel performing a preliminary inspection of the underside of random vehicles by looking under the vehicle through a mirror mounted on a stick, searching for anomalies or foreign objects present on the undercarriage structure and components of a vehicle. If any deviances are detected, the vehicle is directed to a secondary and more thorough inspection. This paper presents a project that aims at automating this preliminary undercarriage inspection by using an automatic under-vehicle inspection system (AUVIS) and image processing algorithms to assist personnel in identifying vehicles for secondary inspection. The inspection system will image the undercarriage of every car in a lane as they approach the CBP agent and use a novel change detection algorithm to compare the captured image to a corresponding reference image of the vehicle's make and model. The software will detect and highlight any differences between the two images to provide a rapid and objective recommendation for secondary inspection.

Unmanned aerial vehicles (UAVs) have become important tools for power transmission line inspection. Cameras installed on the platforms can efficiently obtain aerial images containing information about power equipment. However, most of the existing inspection systems cannot perform automatic real-time detection of transmission line components. In this paper, an automatic transmission line inspection system incorporating UAV remote sensing with binocular visual perception technology is developed to accurately detect and locate power equipment in real time. The system consists of a UAV module, embedded industrial computer, binocular visual perception module, and control and observation module. Insulators, which are key components in power transmission lines as well as fault-prone components, are selected as the detection targets. Insulator detection and spatial localization in aerial images with cluttered backgrounds are interesting but challenging tasks for an automatic transmission line inspection system. A two-stage strategy is proposed to achieve precise identification of insulators. First, candidate insulator regions are obtained based on RGB-D saliency detection. Then, the skeleton structure of candidate insulator regions is extracted. We implement a structure search to realize the final accurate detection of insulators. On the basis of insulator detection results, we further propose a real-time object spatial localization method that combines binocular stereo vision and a global positioning system (GPS). The longitude, latitude, and height of insulators are obtained through coordinate conversion based on the UAV’s real-time flight data and equipment parameters. Experiment results in the actual inspection environment (220 kV power transmission line) show that the presented system meets the requirement of robustness and accuracy of insulator detection and spatial localization in practical engineering.

Defects on the surface of hot rolled steel strips are main factors to evaluate quality of steel strips, an improved image recognition algorithm are used to extract the feature of Defects on the surface of steel strips. Base on the Machine vision and Artificial Neural Networks, establish a defect recognition method to select defect on the surface of steel strips. Base on these research. A surface inspection system and advanced algorithms for image processing to hot rolled strips is developed. Preparing two different fashion to lighting, adopting line blast vidicon of CCD on the surface steel strips on-line. Opening up capacity-diagnose-system with level the surface of steel strips on line, toward the above and undersurface of steel strips with ferric oxide, injure, stamp etc of defects on the surface to analyze and estimate. Miscarriage of justice and alternate of justice rate not preponderate over 5%.Geting hold of applications on some big enterprises of steel at home. Experiment proved that this measure is feasible and effective.

This paper discusses a high-contrast imaging capture system and a wire inspection algorithm for an automatic visual inspection system. On IC assembly lines, automated visual inspection is essential to maintain IC productivity and reliability. An automatic inspection system requires realtime inspection without defects overlooked. To overcome the difficulties of getting clear wire images and inspecting flexible object shapes, we developed a high-contrast imaging capture system and a wire inspection algorithm. The imaging capture system consists of a special camera, called a MegaCamera, by Fujitsu Ltd. , and structured lighting. The MegaCamera satisfied both the requirements of 10-micron-per-pixel inspection resolution and an image capture area of 20 mm square. The structured lighting optic enabled us to get bright wire images and to reduce background noise. The wire inspection algorithm is based on border following. The algorithm enabled us to inspect curved and straight wires and to detect defects including broken wires, too close wires, and incorrect wiring paths. When we installed the system on an actual IC assembly line, it took 28 s to inspect a 68-wire IC. The rate of correct detection is 99.7 percent for ICs and the rate of overlooked defects is 0 percent. These results indicate the system can inspect in real time without defects overlooked, which up to now have required visual inspection.

Performance evaluation of an automatic inspection system of weld defects in radiographic images based on neuro-classifiers

In this paper, an automated inspection system is proposed for detecting the location and measuring the size of existing dipping or buckling on the sandwich panel surface using an RGB camera and an inexpensive linear laser. The proposed method, by observing the radiated laser beams on the sandwich panel surface, can localize and calculate the level of dipping and buckling with acceptable accuracy while being robust to vibrations of moving sandwich panel on the production line conveyor. After a complete processing of the panel by the system, a three-dimensional (surface plot) or two-dimensional (heat map) output is produced to assist the production line supervisor to easily inspect the surface quality of the sandwich panels. Our experimental results show that the proposed system can detect and measure the surface defects including dipping and buckling with a high accuracy and performance.

In this paper we propose an automatic IC leadframe inspection system to inspect major defects in lead spacing, pad shift, tape position and punching defect. This system is composed of an image acquisition part and an inspection part for improvement of inspection speed and flexible inspection environment. We adopted Windows NT operating system in consideration of stability and compatibility of the system. The system has these 2 separate parts so that the image may be refreshed continually during inspection; hence, the whole system's performance was improved. A subpixel method using a high-resolution camera is adopted because the spacing inspection requires very high accuracy and this made it possible stably to maintain an accuracy of about one third of a pixel. Tape inspection is performed by using the mutual relation of the positions of the tape boundary lines. We developed an algorithm which can shrink and expand any polygon efficiently to inspect tapes of arbitrary shapes. By experiment using above inspection method, our inspection system was confirmed as high speed and high accuracy inspection system that can inspect about five leadframe units per second.

Industrial automatic fabric inspection system, a critical technology in the industry, enhances both total production quantity and quality compared to conventional inspection techniques. This study aims to create a reliable and effective real-time automated visual inspection system for fabrics, focusing on defect detection. The goals of the study can be stated as; installing a system with advanced technology for capturing and processing images swiftly, the development and deployment of a system capable of autonomously learning and scanning fabrics in use, and the creation of a smart framework for accurate fabric defect detection and classification. We focus on the development of unsupervised fabric defect detection using a convolutional autoencoder model, and defect classification using a convolutional neural network model, which takes input as the feature vector generated by the convolutional autoencoder. The experimental outcomes have displayed significant success rates in both detecting defects and classifying them, confirming the effectiveness of the framework in real-time visual inspection systems.

A vision inspection system for the surface defects of strongly reflected metal based on multi-class SVM

Automatic optical inspection (AOI) is a control process for precisely evaluating the completeness and quality of manufactured products with the help of visual information. Automatic optical inspection systems include cameras, light sources, and objects; AOI requires expert operators and time-consuming setup processes. In this study, a novel autonomous industrial robot-guided inspection system was hypothesized and developed to expedite and ease inspection process development. The developed platform is an intuitive and interactive system that does not require a physical object to test or an industrial robot; this allows nonexpert operators to perform object inspection planning by only using scanned data. The proposed system comprises an offline programming (OLP) platform and three-dimensional/two-dimensional (3D/2D) vision module. A robot program generated from the OLP platform is mapped to an industrial manipulator to scan a 3D point-cloud model of an object by using a laser triangulation sensor. After a reconstructed 3D model is aligned with a computer-aided design model on a common coordinate system, the OLP platform allows users to efficiently fine-tune the required inspection positions on the basis of the rendered images. The arranged inspection positions can be directed to an industrial manipulator on a production line to capture real images by using the corresponding 2D camera/lens setup for AOI tasks. This innovative system can be implemented in smart factories, which are easily manageable from multiple locations. Workers can save scanned data when new inspection positions are included based on cloud data. The present system provides a new direction to cloud-based manufacturing industries and maximizes the flexibility and efficiency of the AOI setup process to increase productivity.

Automated image inspection system to quantify thermal spray splat morphology