Image Processing Technology Research Articles

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.

Improving the Quality of Digital Images on Identity Cards Using Contrast Stretching and Retinex Methods

Enhancing the image quality of the Indonesian Identity Card is crucial to ensure the clarity and accuracy of information in various applications, such as identity verification, administration, and security. This study aims to improve the digital image quality of Identity Card using the Contrast Stretching and Retinex methods. Contrast Stretching is employed to enhance image contrast, while the Retinex method is used to maintain and improve the clarity of colors and image details. The implementation of these methods is carried out using Python. The test results show that these methods are effective in improving the quality of Identity Card images, making the information on the images clearer and easier to read. This research contributes to the development of more advanced image processing technology, which can be applied to various verification and administrative needs that require high reliability.

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.

Research on the Recognition and Characterization of Fractures in Coal Rock Based on CT Scanning

Abstract Coal rock fractures play a vital role as key channels for gas migration and storage in the exploration and development of coalbed methane. The characteristics of these fractures, such as porosity distribution, specific surface area, and connectivity, directly impact the adsorption, diffusion, and permeability behaviors of coal reservoirs. Therefore, a detailed analysis of coal rock fracture characteristics is vital in assessing the extractability of coalbed methane and during its exploration and development process. Currently, techniques like imaging logging, seismic detection, and micro-CT are employed for characterizing and studying fractures at various scales. Among these, high-resolution non-destructive CT scanning technology, by constructing three-dimensional data models of core samples, enables a quantitative analysis of fracture distribution features and size parameters. Traditional digital image processing methods, such as threshold segmentation and binary segmentation, though advantageous in computational speed, have limitations in the accuracy of fracture identification. With the advancement of artificial intelligence technology, deep learning-based image processing techniques have increasingly become significant tools for fracture detection due to their powerful feature extraction capabilities. This study conducted micrometer-level CT scanning experiments on coal rock samples, using CT image processing technology to construct a digitalized coal rock core model. Deep learning methods were applied for precise fracture identification, establishing an appropriate deep learning architecture and optimized parameters for coal rock fracture recognition. Furthermore, this paper provides a detailed quantitative characterization of the precisely identified fractures, offering robust technical support for the exploration and development of coalbed methane. The high-resolution non-destructive CT scanning technology and three-dimensional fracture network reconstruction method used in this study have been proven to be effective tools for investigating the micro-features of rocks, enabling the three-dimensional visualization and detailed characterization of rock microstructures.

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.

Study of the Dynamic Processes of Multi-point Initiation of Co-axial Explosively Formed Projectiles with Tail Wings Using High Speed Image Processing Technologies

Study of the Dynamic Processes of Multi-point Initiation of Co-axial Explosively Formed Projectiles with Tail Wings Using High Speed Image Processing Technologies

Performance Evaluation of an Object Detection Model Using Drone Imagery in Urban Areas for Semi-Automatic Artificial Intelligence Dataset Construction.

Modern image processing technologies, such as deep learning techniques, are increasingly used to detect changes in various image media (e.g., CCTV and satellite) and understand their social and scientific significance. Drone-based traffic monitoring involves the detection and classification of moving objects within a city using deep learning-based models, which requires extensive training data. Therefore, the creation of training data consumes a significant portion of the resources required to develop these models, which is a major obstacle in artificial intelligence (AI)-based urban environment management. In this study, a performance evaluation method for semi-moving object detection is proposed using an existing AI-based object detection model, which is used to construct AI training datasets. The tasks to refine the results of AI-model-based object detection are analyzed, and an efficient evaluation method is proposed for the semi-automatic construction of AI training data. Different FBeta scores are tested as metrics for performance evaluation, and it is found that the F2 score could improve the completeness of the dataset with 26.5% less effort compared to the F0.5 score and 7.1% less effort compared to the F1 score. Resource requirements for future AI model development can be reduced, enabling the efficient creation of AI training data.

Efficient crop segmentation net and novel weed detection method

Computer vision-based precision weed control offers a promising avenue for reducing herbicide input and the associated costs of weed management. However, the substantial investments in time and labor required for the collection and annotation of weed image data pose challenges to develop effective deep learning models. The limitation also stems from the challenges in achieving accurate and reliable detection of weeds across varying growth stages, densities, and ecotypes in field scenarios. To address these issues, this research investigated a novel methodology employing a segmentation algorithm to accurately mark the contour information of crops in the image and detect weeds through image processing technology. Furthermore, a novel segmentation network was developed based on the YOLO architecture to address the substantial computing resource demands associated with segmentation algorithms. This was achieved through the design of a new backbone, incorporation of an attention mechanism, and modification of the feature fusion technique. The novel network achieved higher segmentation accuracy with less computational demands. The effectiveness of three different attention modules on segmentation tasks was additionally investigated. Experimental results showed that the insertion of Criss-cross Attention significantly improved the model's performance and was subsequently incorporated into our enhanced methodology. The enhanced model achieved a Mean Intersection over Union (mIoU50) of 90.9 %, with precision increasing by 5.9 % and Giga FLoating-point Operations Per Second (GFLOPs) reduced by 15.56 %, demonstrating enhanced suitability for deployment in resource-constrained computing environments. The findings presented in this study hold substantial theoretical and practical implications for precise weed management.

Condition assessment of concrete structures using automated crack detection method for different concrete surface types based on image processing

In the inspection and diagnosis of concrete construction, crack detection is highly recommended in the earliest phases to prevent any potential risks later. However, the flaws in concrete surfaces cannot be reliably and effectively identified using traditional crack detection techniques. The suggested algorithm is a supportive tool for agents or authorities to use in crack detection mechanisms to monitor and assess the current condition of buildings or bridges. The researchers aim to establish an intelligent model for automatic crack detection on different concrete surfaces based on image processing technology. Three different concrete surfaces—bridge decks, walls, and concrete cubes—are used to test the model. A subset of the public dataset of bridge decks and walls from SDNET (2018) and 150*150*150 mm of concrete cubes taken from the material laboratory of the faculty of engineering at Ain Shams University are applied to the model. The model F1-score measures are 98.87%, 97.43%, and 74.11% for detecting cracks in bridges, walls, and concrete cubes, respectively. The validation of the applicability of the suggested novel approach is based on a comparison with recent methods for crack recognition. The contribution of this study is that it could be applied to detect cracks efficiently on three different types of concrete surfaces, including uneven concrete surfaces, random noise, voids, dents, colour changes, and stain marks. The proposed method is transparent in its workflow and has a lower computational cost compared with deep learning frameworks. Thus, the outcomes of this model demonstrate its effectiveness in concrete defect field investigation.