Image Processing Technology Research Articles

Research on Automatic Detection System of Drawing Defects based on Machine Vision

Background: For a long time, product packaging has been used as an instruction manual to connect consumers and factories. Product packaging is an important column in product image display and information presentation. However, missing prints, misprints, and surface stains during the manufacture of packaging bags will cause consumers to misunderstand product information. Based on machine vision, image processing technology, and Python language, this paper designs an automatic detection system for paper defects. Through the preprocessing of the image of the paper to be tested, after the paper area is extracted and compared with the standard template paper, the defective parts of the paper to be tested relative to the standard template paper can be quickly and accurately obtained. The system has a single drawing detection time of 2~3 seconds, and the measurement accuracy rate reaches 100%. The results show that the system has high measurement accuracy, high measurement precision, fast measurement speed, strong adaptability to the environment, and can meet the requirements of detecting defective paper. Objective: The purpose of this study is to develop an automatic detection system for packaging paper, which can detect all defective parts of defective paper compared with standard paper templates. This study aims to reduce the misprints or stains that may occur when producing high-volume bags. The system optimizes and controls the detection accuracy, detection time, detection accuracy and detection environment to ensure that the system can meet the real detection requirements. Method: First, the accompanying software of this system is used to import the standard template of the inspection paper and use the industrial camera to obtain the original image of the inspection drawing. Then, a series of necessary processing is performed on the image: grayscale, Gaussian filter, median filter, binarization, edge detection, contour detection, and the paper area covered with the image is extracted through inverse perspective transformation. Secondly, divide the picture into several blocks and measure the translation matrix of each block to achieve translation fine-tuning to achieve higher detection accuracy. Then, the defect mask is obtained by comparing it with the standard template, and the mask is fine-tuned and processed by the strong noise reduction algorithm. After median filtering, binarization, erosion, marking and other operations are performed to realize the final defect area finding and marking. Finally, all defective areas will be displayed in the designated area of the included software. Results: The detection accuracy rate of this system for the defect area reaches 100%, the minimum range of the recognition area reaches 1mm (2 pixels), the light intensity of the detection environment can adapt to 50 gray levels compared with the template, and the detection of a single drawing only takes 2 ~3 seconds, indicating the high detection efficiency of the system. A patent application for the system has already begun. Conclusion: The system has strong adaptability to the light intensity range of the testing environment, and the minimum testing area can meet the requirements of most production drawings. The accuracy of identifying the defect area of the testing drawings shows that the system can complete the testing task well when the testing environment is suitable.

Intelligent Sentinel satellite image processing technology for land cover mapping

Purpose. This article proposes to develop an intelligent Sentinel satellite image processing technology for land cover mapping using convolutional neural networks. The result will be an image with improved spatial resolution. Methodology. The paper presents a technology using a combination of biquadratic interpolation, histogram alignment, PCA transform, as well as a parallel residual architecture of convolutional neural networks. The technology increases the information content of Sentinel-2 optical images by combining 10 and 20-meter resolution data, resulting in primary 20-meter images with improved spatial resolution. Findings. The root mean square error (RMSE = 3.64) indicates a high accuracy in reproducing the spectral properties of the images. The correlation coefficient (CC = 0.997) confirms a high linear relationship between the estimated and observed images. The low value of Spectral Angle Mapper (SAM = 0.52) with the high Universal Image Quality Index (UIQI = 0.999) indicates high quality and structural similarity between the synthesized and reference images. These results confirm the proposed technology’s effectiveness in enhancing the spatial resolution of Sentinel satellite images. Originality. Traditional pansharpening methods of multispectral images developed for satellite images with panchromatic channels cannot be directly applied to Sentinel multispectral data, because these images do not contain a panchromatic channel. In addition, atmospheric conditions and the presence of clouds affect the quality of optical images, complicating their further thematic processing. The proposed technology, using biquadratic interpolation, histogram alignment, convolutional neural networks, and PCA transformation, removes clouds and enhances the spatial resolution of the primary 20-meter optical satellite image channels of Sentinel-2. This technology reduces color distortion and increases the detail of digital optical images, which allows for more accurate analysis of the state of the earth’s surface. Practical value. The results obtained can be used to improve the methods for processing Sentinel satellite images, which provide high spatial resolution and accurate preservation of spectral characteristics. It provides the foundation for the development of new geographic information systems for land cover monitoring.

A Novel Liquid–Solid Fluidized Bed of Large-Scale Phase-Changing Sphere for Thermal Energy Storage

The storage of thermal energy has been hindered by the low heat-transfer rate of the solid phase of the phase-changing materiel. With water being the heat-transfer fluid as well as the liquid phase in the liquid–solid two-phase system, a novel type of fluidized bed is designed in this study. Numerous hollow spheres are fabricated with phase-changing materiel encapsulated. Adding the solid–liquid phase-change material capsules to the flowing fluid, the capsules are dispersed suspended in the carrier. The large spheres, 25 mm in present experiment, possess the merits of guaranteeing energy-storage density and tolerating internal interface chaotic motion. Both the fluidization status and phase-changing process are recorded by photography combined with image-processing technology. It is found that the large spheres, with density less than water, can be fluidized by the downward flowing fluid. As the flow rate increases, the expansion ratio of the solid phase increases and the regimes of incipient fluidization and bubbling fluidization can be observed. In comparison to the fixed bed, the oscillation of pressure drop across a fluidized bed is more severe, but the averaged value is less than the fixed bed. The melting and solidifying can be accelerated by 22.6% and 50%, respectively, thus proving the superiority of the fluidized bed in improving the heat-transfer rate while charging/discharging the thermal energy. Three types of basic movement of the spheres are shown to contribute to the enhanced phase-changing rate, which are shifting, colliding and rotating.

LiqD: Dynamic Liquid Level Detection under Containers

Abstract. In daily life and industrial production, it is crucial to accurately detect changes in liquid level in containers. Traditional contact measurement methods have some limitations, while emerging non-contact image processing technology shows good application prospects. This paper proposes a container dynamic liquid level detection model based on U-Net. This model uses the SAM model to generate an initial data set, and then evaluates and filters out high-quality pseudo-label images through the semi-supervised framework to build an exclusive data set. The model uses U-Net to extract masking images of containers from the data set, and uses morphological processing to compensate for masking defects. Subsequently, the model calculates the grayscale difference between adjacent video frame images at the same position, segments the liquid level change area by setting a difference threshold, and finally uses a lightweight neural network to classify the liquid level state and achieves the accuracy of 91.39%. This approach not only mitigates the impact of intricate surroundings, but also reduces the demand for training data, showing strong robustness and versatility. A large number of experimental results show that the proposed model can effectively detect the dynamic liquid level changes of the liquid in the container, providing a novel and efficient solution for related fields.

Automated volumetric analysis of the inner ear fluid space from hydrops magnetic resonance imaging using 3D neural networks

Due to the development of magnetic resonance (MR) imaging processing technology, image-based identification of endolymphatic hydrops (EH) has played an important role in understanding inner ear illnesses, such as Meniere’s disease or fluctuating sensorineural hearing loss. We segmented the inner ear, consisting of the cochlea, vestibule, and semicircular canals, using a 3D-based deep neural network model for accurate and automated EH volume ratio calculations. We built a dataset of MR cisternography (MRC) and HYDROPS-Mi2 stacks labeled with the segmentation of the perilymph fluid space and endolymph fluid space of the inner ear to devise a 3D segmentation deep neural network model. End-to-end learning was used to segment the perilymph fluid and the endolymph fluid spaces simultaneously using aligned pair data of the MRC and HYDROPS-Mi2 stacks. Consequently, the segmentation performance of the total fluid space and endolymph fluid space had Dice similarity coefficients of 0.9574 and 0.9186, respectively. In addition, the EH volume ratio calculated by experienced otologists and the EH volume ratio value predicted by the proposed deep learning model showed high agreement according to the interclass correlation coefficient (ICC) and Bland–Altman plot analysis.

Broadband and parallel multiple-order optical spatial differentiation enabled by Bessel vortex modulated metalens

Optical analog image processing technology is expected to provide an effective solution for high-throughput and real-time data processing with low power consumption. In various operations, optical spatial differential operations are essential in edge extraction, data compression, and feature classification. Unfortunately, existing methods can only perform low-order or selectively perform a particular high-order differential operation. Here, we propose and experimentally demonstrate a Bessel vortex modulated metalens composed of a single complex amplitude metasurface, which can perform multiple-order radial differential operations over a wide band by presetting the order of the corresponding Bessel vortex. This architecture further enables angle multiplexing to create multiple information channels that synchronously perform multi-order spatial differential operations, indicating the superiority of the proposed devices in parallel processing. Our approach may find various applications in artificial intelligence, machine vision, autonomous driving, and advanced biomedical imaging.

Assembly method for curved porous circular workpieces based on multidimensional information fusion: Grid electrode component

To tackle the problems related to the complexities of obtaining depth information for circular workpiece with arc surface and holes, a novel vision guidance method has been introduced to precisely gauge interlayer spacing. The process includes the acquisition and preprocessing of point cloud depth information, along with matching and edge processing. The depth coordinates of the point cloud are subsequently determined. Two-dimensional image processing technology is employed to extract the coordinates of the center of the surface circular hole in the curved porous circular workpiece. The 3D coordinates are generated for guiding the assembly by integrating these datasets. Experiments on grid parts in aviation propulsion systems demonstrate that this method maintains dot matrix spacing within 0.5–1.2 mm, ensures the coaxiality of the center hole, and meets assembly standards. The results confirm the method’s effectiveness for automated assembly of curved porous round workpieces.

Vibration Position Detection of Robot Arm Based on Feature Extraction of 3D Lidar.

In the process of construction, pouring and vibrating concrete on existing reinforced structures is a necessary process. This paper presents an automatic vibration position detecting method based on the feature extraction of 3D lidar point clouds. Compared with the image-based method, this method has better anti-interference performance to light with reduced computational consumption. First, lidar scans are used to capture multiple frames of local steel bar point clouds. Then, the clouds are stitched by Normal Distribution Transform (NDT) for preliminary matching and Iterative Closest Point (ICP) for fine-matching. The Graph-Based Optimization (g2o) method further refines the precision of the 3D registration. Afterwards, the 3D point clouds are projected into a 2D image. Finally, the locations of concrete vibration points and concrete casting points are discerned through point cloud and image processing technologies. Experiments demonstrate that the proposed automatic method outperforms ICP and NDT algorithms, reducing the mean square error (MSE) by 11.5% and 11.37%, respectively. The maximum discrepancies in identifying concrete vibration points and concrete casting points are 0.059 ± 0.031 m and 0.089 ± 0.0493 m, respectively, fulfilling the requirement for concrete vibration detection.

YOLOv5s-Based Image Identification of Stripe Rust and Leaf Rust on Wheat at Different Growth Stages.

Stripe rust caused by Puccinia striiformis f. sp. tritici and leaf rust caused by Puccinia triticina, are two devastating diseases on wheat, which seriously affect the production safety of wheat. Timely detection and identification of the two diseases are essential for taking effective disease management measures to reduce wheat yield losses. To realize the accurate identification of wheat stripe rust and wheat leaf rust during the different growth stages, in this study, the image-based identification of wheat stripe rust and wheat leaf rust during different growth stages was investigated based on deep learning using image processing technology. Based on the YOLOv5s model, we built identification models of wheat stripe rust and wheat leaf rust during the seedling stage, stem elongation stage, booting stage, inflorescence emergence stage, anthesis stage, milk development stage, and all the growth stages. The models were tested on the different testing sets in the different individual growth stages and in all the growth stages. The results showed that the models performed differently in disease image identification. The model based on the disease images acquired during an individual growth stage was not suitable for the identification of the disease images acquired during the other individual growth stages, except for the model based on the disease images acquired during the milk development stage, which had acceptable identification performance on the testing sets in the anthesis stage and the milk development stage. In addition, the results demonstrated that wheat growth stages had a great influence on the image identification of the two diseases. The model built based on the disease images acquired in all the growth stages produced acceptable identification results. Mean F1 Score values between 64.06% and 79.98% and mean average precision (mAP) values between 66.55% and 82.80% were achieved on each testing set composed of the disease images acquired during an individual growth stage and on the testing set composed of the disease images acquired during all the growth stages. This study provides a basis for the image-based identification of wheat stripe rust and wheat leaf rust during the different growth stages, and it provides a reference for the accurate identification of other plant diseases.

Application of ROI-based image processing technology in mechanical component size measurement

Application of ROI-based image processing technology in mechanical component size measurement

The Application of Computed Tomography to Study the Soil Porosity of Mountain Red Earth

Mountain red soil, as a special type of soil in the South, has received widespread attention for its soil erosion problems. Its pore structure restricts water infiltration, thereby affecting the occurrence and development of soil erosion. In order to systematically obtain the distribution characteristics of the pore structure within the surface mountain red soil, this paper uses non-destructive CT detection technology to scan the soil column samples taken from the typical mountain red soil distribution area in Chenggong District, Kunming City, Yunnan Province. Image processing technology is applied to CT slices, and ImageJ (1.46r) software is used to obtain the distribution characteristics of pores within the soil column, including pore sizes and the number of pores at each depth, the proportion of pore area, roundness, and box-counting dimension. The results show that with the increase in depth, the proportion of pore area decreases linearly from the maximum value of 52.25% at the top to the minimum value of 2.02% at the bottom; the roundness of pores fluctuates between 0.8 and 0.9, overall increasing; the total number of pores generally first increases then decreases, and small pores are predominant, with the least number of large pores in the topsoil layer; the box-counting dimension shows a gradual linear decrease, with a maximum value of 1.7980 and a minimum value of 0.9878. The number of pores affects both roundness and the box-counting dimension, and the proportion of pore area also affects the box-counting dimension. There is a negative correlation between roundness and the box-counting dimension. The 3D visualization reconstruction of pores shows that most are interconnected, with the pore size significantly reducing with increasing depth. The quantitative analysis of parameters and 3D visualization reveal, to some extent, the impact of pore structure on the occurrence and development of soil erosion in mountain red soil. These research findings form the foundation for studying soil erosion in this region and provide a basis for systematically understanding its processes and mechanisms.

Multi-Scale Feature Attention Fusion for Image Splicing Forgery Detection

Image splicing is a widely occurrence image tampering technology. With the rapid development of digital image processing technology, detecting image splicing forgery has become significantly challenging. Although various methods have been devised to identify such tampered images, existing approaches have not achieved optimal performance due to limitations in effectively leveraging feature maps of different scales. To address this issue, we propose a novel method for image splicing forgery detection called multi-scale feature attention fusion network (MFAF-Net). We propose a multi-scale atrous feature attention (MAFA) module designed to capture rich contextual features for multi-scale high-level feature fusion. Additionally, we present the multi-branch attention mechanism (MBAM) module to fuse contextual information from various branches for low-level features. This integration enhances the capability of low-level features to produce more refined pixel-level attention. We employ the weighted binary cross-entropy loss and dice loss in the MFAF-Net to overcome the imbalance between positive and negative samples. Extensive experiments demonstrate that the proposed MFAF-Net outperforms state-of-the-art methods. Robustness experiments also show our model exhibits image splicing forgery detection robustness under common attacks.

Design and Fabrication of SCARA for Image Processing in Industry

The scope of our project is the design and fabrication of SCARA (Selective Compliance Articulated Robot Arm) robots optimized for image processing tasks within industrial environments. This report presents a comprehensive review of the design, control, and applications of SCARA robots, focusing on their integration with image processing technologies to enhance industrial automation. We delve into the underlying principles governing the kinematics and dynamics of SCARA robots, elucidating their operational mechanisms and capabilities. Advancementsin end-effector design and sensor integration have enhanced the adaptability of SCARA robots, enabling them to tackle complex tasks in unstructured environments.

WSSS-CRAM: precise segmentation of histopathological images via class region activation mapping.

Fast, accurate, and automatic analysis of histopathological images using digital image processing and deep learning technology is a necessary task. Conventional histopathological image analysis algorithms require the manual design of features, while deep learning methods can achieve fast prediction and accurate analysis, but rely on the drive of a large amount of labeled data. In this work, we introduce WSSS-CRAM a weakly-supervised semantic segmentation that can obtain detailed pixel-level labels from image-level annotated data. Specifically, we use a discriminative activation strategy to generate category-specific image activation maps via class labels. The category-specific activation maps are then post-processed using conditional random fields to obtain reliable regions that are directly used as ground-truth labels for the segmentation branch. Critically, the two steps of the pseudo-label acquisition and training segmentation model are integrated into an end-to-end model for joint training in this method. Through quantitative evaluation and visualization results, we demonstrate that the framework can predict pixel-level labels from image-level labels, and also perform well when testing images without image-level annotations. Future, we consider extending the algorithm to different pathological datasets and types of tissue images to validate its generalization capability.

A systematic investigation of the freezing process of silty clay through unidirectional stepwise freezing method combined with digital imaging processing technology

Soil freezing represents a complex thermo-hydro-mechanical coupling process that encompasses a range of physical and mechanical phenomena, such as heat transfer, moisture migration, cryostructure development, ice lens segregation, frost heave, and consolidation, all of which interact and influence one another throughout the freezing process. This study conducted a unidirectional stepwise freezing test, comprising three distinct freezing stages, to investigate the freezing behavior of Qinghai-Tibet silty clay using digital imaging processing technology (DIPT), which includes an image acquisition system and a digital image processing system. The results demonstrate that the stepwise freezing test provides a more comprehensive insight into the physical and mechanical processes occurring during unidirectional soil freezing. At various stages of freezing, the temperature distribution within the sample achieves a linear stabilization, with the frozen zone exhibiting a slightly larger extent compared to the unfrozen zone. The longitudinal section of the sample developed layered cryogenic cracks, whereas the horizontal section revealed interconnected cryogenic polygonal cracks of varying sizes, which facilitated moisture migration and ice segregation. Furthermore, ice lenses can segregate within a broad temperature range below the soil freezing point in the frozen zone, resulting in frost heave within the frozen zone and consolidation of the unfrozen zone. This study proposes an experimental method to clarify the complex phenomenon and mechanisms of frost heave during soil freezing.

Design and Practice of Virtual Experimental Scenes Integrating Computer Vision and Image Processing Technologies

In this work, we introduce a novel framework, GAN-based Image Processing (GAN-IP), to design and manipulate digital experimental settings that effectively combine computer vision and image processing technology. Using the skills of Generative Adversarial Networks (GANs), GAN-IP creates and complements virtual scenes and affords rich, adaptive surroundings for analysing and creating computationally smart and perceptive algorithms. GAN-IP solves the pressing troubles of variability and absence of data through synthesising realistic pictures and scenarios, enabling simulations of the diverse environments in which computer vision structures have to function. Our approach improves the fidelity and sort of digital scenes and introduces a way to mechanically alter and evoke a pleasant image, enabling more accurate and powerful computer vision and prescient models. Through extensive experimentation, GAN-IP demonstrates remarkable improvements in the performance of computer vision tasks, including object detection, segmentation, and recognition in complicated virtual environments. This research lays the foundation for future studies in this field and provides an adaptive tool for researchers and practitioners to simulate and test superior computer imaginative and prescient image processing technologies.

Impact of liquid crossflow on the discharge coefficient of a gas jet hole on a flat plate

This study combines the experimental and numerical simulation methods to deeply analyze the impact of liquid crossflow on the discharge coefficient of a gas jet hole on a flat plate. Experiments were conducted to examine the influence of momentum flux ratio and theoretical momentum flux ratio on the discharge coefficient under various crossflow Reynolds numbers. It was found that the variation of the discharge coefficient with the theoretical momentum flux ratio clearly reflects the impact of the crossflow boundary layer velocity profile on the discharge coefficient. The rapid growth of velocity in the boundary layer near the wall in the direction normal to the wall surface, or the decrease in the thickness of the boundary layer, both enhance the shearing effect of the crossflow, leading to a decrease in the discharge coefficient. Analysis of the cavity morphology at the hole exit captured by high-speed camera revealed that the averaged profile of the gas–liquid boundary on the symmetrical plane of the jet below the hole can be approximated as a straight line within the scale of the hole diameter, and the sine of the angle between this line and the upper wall surface is roughly equivalent to the normalized discharge coefficient. This relationship was physically interpreted through the analysis of effective and equivalent flow cross-sectional shapes derived from numerical simulation at different crossflow Reynolds numbers and theoretical momentum flux ratios. Additionally, this paper introduces an innovative method for predicting jet flow rate based on image processing technology. A notable feature of this method is that it does not require the measurement of the pressure inside the gas chamber.

Research on Algorithm of Composite Material Painting Creation based on Image Processing Technology

In the study we proposed a novel approach is called a Customized Convolutional Neural Network (CCNN) to innovate in the field of art creation, particularly in composite material paintings. This research harnesses the power of image processing technology to analyze and synthesize various artistic elements, thereby facilitating the creation of composite material paintings. The core of the study revolves around the development of a unique algorithm that enables the integration of diverse materials and textures into a cohesive artistic expression. The Customized CNN is trained on a vast dataset of images, encompassing a wide spectrum of textures, colors, and patterns, representative of different materials commonly used in art. The network learns to identify and replicate the aesthetic qualities of these materials, thereby empowering artists to explore new realms of creativity. The algorithm not only recognizes the distinct characteristics of each material but also understands how to blend them effectively, maintaining artistic coherence. The results are evaluated to prove proposed performance.

Design and testing of a low-cost mobile platform for contactless building surveying

After the completion of building construction, evaluating the accuracy of the distance between the lower edge of electrical switches and the one-meter line of the building is a crucial metric for assessing construction quality. Currently, the traditional measurement method basically uses manual measurement and is inefficient. To overcome this problem, we present a low-cost, non-contact mobile platform for building measurement, based on a 2D laser rangefinder and RGB camera. The system mainly consists of six key components: a mobile chassis, dual-axis gimbal, laser rangefinder, RGB camera, industrial control computer, and an automatic one-meter line calibration device, which can achieve high-precision distance measurement. Firstly, a sensorless control method for brushless DC motors (BLDCM) based on the Extended Kalman Filter (EKF) algorithm is proposed, which effectively improves the accuracy and robustness of rotor position estimation. Secondly, an automatic calibration device is developed to ensure the laser line consistently remains at a height of one meter from the ground. Finally, by combining image processing technology and 2D laser range data, the distance between the lower edge of the electrical switch and the building’s one-meter line is precisely measured using a triangulation algorithm. Experimental results show that the system’s measurements, compared to manual measurements, have a relative error percentage of 0.57%, while the automatic calibration device has a maximum error of only 0.2 centimeter. Therefore, the system shows great potential for widespread application in the field of building measurement.

Calculating the focal length of a concave mirror computerically using mathematical algorithms using MATLAB

This study proposes the use of digital image processing technology to determine the focal lengths of simple concave mirror , as well as spherical mirrors. This method is simpler, faster to implement, and more accurate in calculating the focal length by taking several pictures at different distances for the image formed by the lens, then calculating the least point spread function for these pictures at that distance to determine the best image. In this work Image No. (7) represents the image that carries the least (psf) that was determined by algorithms and special mathematical equations through the (Matlab) program, then the focal length is calculated by employing algorithms and mathematical equations specific to calculating the focal length. A comparison was made with the traditional method by taking measurements of a group of students at the Optics laboratory of University of Wasit, College of Science (which depends on view to choose the best image). This method has proven to be superior in measuring the focal length of lenses. A statistical analysis was conducted for these measurements for the group of students and it was found that there was differences measurements with an error rate, which confirms the feasibility of this method in terms of measurement accuracy and time saving. Experimental results have confirmed this.