Machine Vision Applications Research Articles

Research on Application of Machine Vision in Robots for Live-Power Lines

The electric fire-exchanging operation is basically used to implement operation such as Wire stripping, drainage wire through the fastening wire clip, clamp bolt fastening, etc. Manual operation is time-consuming and laborious and has very high safety risks, so the automatic electrified ignition robot emerges as The Times require; The automatic operation method should accurately identify the position of wire and drainage line, so as to guide the robot to move and carry out the corresponding operation. Based on this, an intelligent binocular vision guidance method is proposed in this paper. The image data in the working space is collected by the binocular camera in real time, the wire is identified by the image segmentation technology, the wire is separated from the background, and the spatial pose of the wire is solved by the PNP and binocular stereo vision technology. Through YOLOV5 target detection, the position identification and positioning of the thread and clamp of the drainage line are realized, and the automatic fire ignition operation is finally guided by the manipulator. Experiments show that this method has the advantages of high accuracy, good stability and fast speed.

Application of Target Image Recognition under MRI Filtering Technology

Image recognition is an important foundation of computer vision, and has a wide range of applications in machine vision, traffic monitoring, and intelligent drones. At present, there are many image recognition methods, each with its own advantages under different conditions. It is worth noting that in the field of image recognition, speed and recognition rate are two important indicators for evaluating the quality of image recognition methods. Generally speaking, the speed and recognition rate of image recognition methods are not only related to the corresponding algorithms, but also related to the computational performance of on-site image processing equipment.

Engineering tunability through electro-optic effects to manifest a multifunctional metadevice.

Ultrafast modulation of the refractive index exhibits either linear or nonlinear electro-optic (EO) effect, which is extensively utilized in tunable photonic circuits. Silicon, a mature material for on-chip devices, lacks a strong electro-optic (EO) Pockels effect. Utilization of the Pockels effect alters the intrinsic property (refractive index) of the material that manifests tunability and offers expanded functionalities. Driven by the limited space constraints in data storage, sensing, and imaging applications, we propose an electrically tunable meta-device whose resonance wavelength and focal length can be tuned by varying applied electric fields in the visible range. The fundamental unit of a metalens is the barium titanate (BTO) diffractive optical element placed on indium titanium oxide (which serves as an electrode) with SiO2 as the substrate. The metalens' tunability is characterized by point spread function (PSF), full-width half-maximum (FWHM), and imaging bandwidth that demonstrates the tuning of resonance wavelength and focal length. Moreover, polarization-insensitive meta-holograms are realized at a wavelength of 633 nm without utilizing propagation and Pancharatnam–Berry (PB) phase. The proposed study can find exciting applications in machine vision, broadband microscopy, and spectroscopy.

Circular coded target system for industrial applications

Coded targets are used as reference targets with a known location during camera calibration, for robust searching of corresponding features between images during various applications of machine vision like object tracking, robot navigation or 3D measurement. In this paper, a target system for industrial photogrammetric applications is outlined. The methods which have been chosen emphasize maximum robustness, along with accuracy at the expense of computational efficiency, since photogrammetric measurements are mainly evaluated offline. The outlined system combines widely used photogrammetric circular coded target design with an automatic library generator. It also utilizes robust methods of target detection and recognition with error correction (in case of 60 15-bit targets, up to 1 bit confused or 2 bits occluded) along with preserving the low false-positive or confusion rate. Any error correction method for this type of targets was not introduced before. The solution also allows for the creation of versatile target libraries or to work with the existing target libraries of a number of commercial photogrammetric systems. The properties of the target system were tested under challenging conditions (including heavy noise, blur, occlusion and geometrical image transformations) and compared to state-of-the-art systems, e.g. TRITOP (GOM), or ArUco, which it outperforms. The target system is already used for the camera calibration of a specialized photogrammetric system utilized in the heavy industry environment.

Application of machine vision in COB LED display module ink collection, classification and sorting

Application of machine vision in COB LED display module ink collection, classification and sorting

IDCube Lite: Free Interactive Discovery Cube software for multi- and hyperspectral applications.

Multi- and hyperspectral imaging modalities encompass a growing number of spectral techniques that find many applications in geospatial, biomedical, machine vision and other fields. The rapidly increasing number of applications requires convenient easy-to-navigate software that can be used by new and experienced users to analyse data, and develop, apply and deploy novel algorithms. Herein, we present our platform, IDCube Lite, an Interactive Discovery Cube that performs essential operations in hyperspectral data analysis to realise the full potential of spectral imaging. The strength of the software lies in its interactive features that enable the users to optimise parameters and obtain visual input for the user in a way not previously accessible with other software packages. The entire software can be operated without any prior programming skills allowing interactive sessions of raw and processed data. IDCube Lite, a free version of the software described in the paper, has many benefits compared to existing packages and offers structural flexibility to discover new, hidden features that allow users to integrate novel computational methods.

Literature Review of Machine Vision in Application Field

Aiming at the application and research of machine vision, a comprehensive and detailed elaboration is carried out in its two application areas: visual inspection and robot vision. Introduce the composition, characteristics and application advantages of the machine vision system. Based on the analysis of the current research status at home and abroad, the application development trend of machine vision is prospected.

Editorial to the Special Section on Optimized Image/Video Coding Based on Deep Learning

This Special Section of the IEEE Open Journal of Circuits and Systems is dedicated to a collection of articles on Optimized Image/Video Coding based on Deep Learning, to promote techniques in algorithms, implementations, and applications in this highly active field of research. This Special Section covers articles in three categories including end-to-end image/video coding optimization, learning techniques for optimizing traditional image/video codecs, and coding for machine vision applications.

An Autonomous Illumination System for Vehicle Documentation Based on Deep Reinforcement Learning

A common problem for machine vision applications is uncontrolled illumination conditions that cause undesired artifacts on sensorial data. For instance, quality inspection using color cameras, while having wide industrial application, requires manual illumination adjustment and is severely affected by external lighting sources and the physical properties of the inspected object. To overcome this problem, we propose an autonomous illumination solution, that adjusts illumination via a Deep Reinforcement Learning (DRL) agent following a goal-oriented reward that takes into account image entropy and specularity. The system is validated in a challenging vehicle documentation use case where vehicle images are captured under various lighting conditions using a camera and an in-house built illumination system. The DRL agent learns to control illumination levels directly from high-dimensional visual inputs by mapping the interactions from the environment to the reward-driven control actions of the illumination system, targeting an optimal illumination zone even under the appearance of abrupt illumination changes in the environment.

Scale-, Shift-, and Rotation-Invariant Diffractive Optical Networks

Recent research efforts in optical computing have gravitated towards developing optical neural networks that aim to benefit from the processing speed and parallelism of optics/photonics in machine learning applications. Among these endeavors, Diffractive Deep Neural Networks (D2NNs) harness light-matter interaction over a series of trainable surfaces, designed using deep learning, to compute a desired statistical inference task as the light waves propagate from the input plane to the output field-of-view. Although, earlier studies have demonstrated the generalization capability of diffractive optical networks to unseen data, achieving e.g., >98% image classification accuracy for handwritten digits, these previous designs are in general sensitive to the spatial scaling, translation and rotation of the input objects. Here, we demonstrate a new training strategy for diffractive networks that introduces input object translation, rotation and/or scaling during the training phase as uniformly distributed random variables to build resilience in their blind inference performance against such object transformations. This training strategy successfully guides the evolution of the diffractive optical network design towards a solution that is scale-, shift- and rotation-invariant, which is especially important and useful for dynamic machine vision applications in e.g., autonomous cars, in-vivo imaging of biomedical specimen, among others.

Using Deep Learning to Detect Defects in Manufacturing: A Comprehensive Survey and Current Challenges.

The detection of product defects is essential in quality control in manufacturing. This study surveys stateoftheart deep-learning methods in defect detection. First, we classify the defects of products, such as electronic components, pipes, welded parts, and textile materials, into categories. Second, recent mainstream techniques and deep-learning methods for defects are reviewed with their characteristics, strengths, and shortcomings described. Third, we summarize and analyze the application of ultrasonic testing, filtering, deep learning, machine vision, and other technologies used for defect detection, by focusing on three aspects, namely method and experimental results. To further understand the difficulties in the field of defect detection, we investigate the functions and characteristics of existing equipment used for defect detection. The core ideas and codes of studies related to high precision, high positioning, rapid detection, small object, complex background, occluded object detection and object association, are summarized. Lastly, we outline the current achievements and limitations of the existing methods, along with the current research challenges, to assist the research community on defect detection in setting a further agenda for future studies.

RETRACTED: Exploring machine vision application in public art education system based on image processor

This article presents an interdisciplinary examination that centers on the aftereffects of public craftsmanship and quality instruction. It brought up issues about open craftsmanship as an instructive asset and perceived the need to explain its instructive job. This examination researched whether numerous metropolitan climate works and public craftsmanship should positively commit to visual education advancement. The Field Programmable Gate Array (FPGA) is a coordinated circuit with client programmable interconnected inward equipment squares to tweak tasks for explicit applications. The overview was directed in a spot where there are many available quality and three schools in the metropolitan space. There are not many five and six understudies in three schools. One end is that understudies in the school climate have numerous public works capable of metropolitan space recognition and more instinctive proficiency. This isn't just because of their day-by-day contact with in addition to social variables. In light of the examination results, the creator delivered instructive assets for instructors and guardians keen on building up kids' visual education.

Switchable polarization-multiplexed super-oscillatory metasurfaces for achromatic sub-diffraction focusing.

Super-oscillation phenomenon has attracted considerable interests due to its great ability of far-field super-resolution imaging. However, most super-oscillatory lenses were limited by chromatic aberration and single functionality, hence deeply restricting the flexibility of the super-oscillatory devices in practical applications. Here, an achromatic polarization-multiplexed super-oscillatory metasurface has been proposed to realize flexible light field modulations at different colors, i.e. 473 nm (blue), 532 nm (green), and 632.8 nm (red). The super-oscillatory metasurface can achieve achromatic diffraction-limited focusing under x-polarized light illumination and achromatic sub-diffraction focusing under y-polarized light illumination. Furthermore, it can also realize multi-wavelength super-oscillatory achromatic focusing with different super-resolution abilities. The proposed method could simplify the super-resolution optical imaging system and is expected to have widespread applications in color imaging, microscopy, and machine vision.

Efficient image segmentation method based on an adaptive selection of Gabor filters

Image segmentation is one of the most important aspects of image processing that has many applications in image analysis, computer vision, and machine vision. So far, researchers have proposed many image segmentation algorithms that provide a relatively appropriate response for a particular application and over a specific type of input images. At the same time, they have not yet suggested a simple, fast, and efficient segmentation method that can apply to a wide range of natural and texture images. In this study, the authors present a new adaptive method for selecting Gabor filters using the image frequency content. Adaptive spectral features of the image are then extracted using only these selected filters from a Gabor filter bank. A pre-setting parameter Gabor filter is used to extract the gradient of each feature in x and y directions. Combining these features gradient, the texture gradient image is calculated, and then by selected markers for different texture regions and applying the marker-based watershed transform on texture gradient image, the authors present a new image segmentation method that has a less computational cost and yields better results compared to using all other filter banks.

Smart Line Scan Measurement Systems

AbstractCurrent industrial machine vision applications mostly rely on PC‐based image processing. Smart camera systems, which incorporate an image sensor and computing capabilities, were initially restricted to very simple tasks like barcode reading and interpretation. But as the computing capabilities of embedded systems have increased, smart cameras are now able to handle a wide range of applications.

Research on 5G+MEC Industrial Internet Technology Testing Platform

The 5G+MEC Industrial Internet technology test platform is committed to the application of 5G technology in industrial scenarios. The test platform is supported by 5G+MEC as the technical architecture, and is integrated with machine vision, AR, AGV and other applications; The test platform adopts a modular design. Starting from the test scenarios of the manufacturing process such as interaction, customization, and production, some technologies are transformed into mature solutions through testing, which shortens the application cycle of new technologies and promotes the development of the Industry. Internet.

Oral and Dental Spectral Image Database—ODSI-DB

The most common imaging methods used in dentistry are X-ray imaging and RGB color photography. However, both imaging methods provide only a limited amount of information on the wavelength-dependent optical properties of the hard and soft tissues in the mouth. Spectral imaging, on the other hand, provides significantly more information on the medically relevant dental and oral features (e.g. caries, calculus, and gingivitis). Due to this, we constructed a spectral imaging setup and acquired 316 oral and dental reflectance spectral images, 215 of which are annotated by medical experts, of 30 human test subjects. Spectral images of the subjects’ faces and other areas of interest were captured, along with other medically relevant information (e.g., pulse and blood pressure). We collected these oral, dental, and face spectral images, their annotations and metadata into a publicly available database that we describe in this paper. This oral and dental spectral image database (ODSI-DB) provides a vast amount of data that can be used for developing, e.g., pattern recognition and machine vision applications for dentistry.

Application of machine vision in the tasks of automatic positioning of modular equipment tool

Application of machine vision in the tasks of automatic positioning of modular equipment tool

Calibration method for the electrically tunable lens based on shape-changing polymer.

In this paper, a calibration method for the camera system with electrically tunable lens (ETL) based on shape-changing polymer (SCP) is proposed to improve the accuracy, robustness and practicality of the system. The camera model of the ETL based on SCP is proposed based on the analyses of its optical properties. The calibration strategy, including initial estimation of camera parameters and bundle adjustment is presented. To eliminate the influence of temperature on ETL in machine vision applications, a real-time temperature compensation method is proposed. The proposed method makes use of the existing calibration hardware without adding new components to the system. Both simulations and experiments are conducted to evaluate the effectiveness and accuracy of the proposed camera model and calibration method. The measurement error with the proposed calibration method is below 20 microns at high magnification, whose measurement accuracy is improved by five times than the existing method at high magnification. With the proposed calibration method for the camera system with ETL based on SCP, the calibration workload is reduced and accurate calibration at high magnification is achieved. It also benefits the development of autofocusing 3D measurement technology.

Application of Machine Vision in the Inspection System of Steel Plate Surface Defects

Although my country is a big country in steel production, the development of domestic steel plate surface defect detection technology is backward, and most small and medium-sized enterprises are still at the stage of manual visual inspection. This paper investigated by machine vision defects in the steel sheet surface detection system application, in order to improve the steel surface defect detection rate. In this paper, a set of steel plate surface defect detection system is proposed based on machine vision, the hardware modules and selection methods of the system are designed, and four surface defects of holes, scratches, zinc scars and cracks are verified, and good experimental results are obtained. Using three different speeds for detection, the detection rate of steel surface defects reached more than 90%, and the recognition rate reached more than 80%. Research shows that the system in this paper can detect defects on the surface of steel plates well.