Image Processing Technology Research Articles

The ubiquitous influence of image recognition in daily life

Image recognition technology has emerged due to the quick advancement of computer science. Using image recognition technology, a computer can identify and categorize diverse items in an image according to their functions. Artificial intelligence, image processing, and image recognition technology are all combined in this technology. The human eye and image recognition have a similar fundamental operating principle. The best outcomes can be achieved by taking out a few crucial elements from the image and contrasting them with the data that has been captured. This study describes how image recognition technology is used widely in modern life. It can improve people's lives if picture recognition is quick and precise. However, there are still many issues with picture recognition technology, such as security issues with gathering private information and loss of objectivity owing to algorithmic bias. Image recognition technology will become increasingly crucial in the connected world due to the ongoing development and enhancement of computer power. Image recognition technology will push what is technically possible in visual perception and artificial intelligence.

Application of Hybrid Image Processing Based on Artificial Intelligence in Interactive English Teaching

Primary school English teaching resources play an important role in primary school English teaching. The information age requires that primary school English teaching should strengthen the use of multimedia resources and gradually realize the diversification of teaching content. Expanded reality innovation is a sort of mixture picture handling innovation, which is one of the significant innovations that would influence the improvement of fundamental schooling in the following five years. It can seamlessly output virtual objects to the real environment, which is convenient for this paper to obtain and absorb information. It can also help students to participate in exploration and cultivate their creativity and imagination. It can strengthen the cooperation between students and teachers and create various learning environments. It has an immeasurable prospect of development in the field of education. The primary school English teaching resources based on augmented reality create a realistic learning situation from two-dimensional plane to three-dimensional three-dimensional display, and enrich the presentation of primary school English teaching content. It can stimulate students’ interest in learning English and promote the transformation of English teaching methods. It is a useful attempt in the field of education. This paper made statistics on the test results of the experimental class and the control class. Most of the scores of the experimental group were between 71 and 100, a total of 27, accounting for 67.5%. The score distribution of the control class was relatively balanced, with the highest number between 61-70, and the number was 10, accounting for 25%. Therefore, it can be seen that hybrid image processing technology is important for interactive English teaching.

A Method for Analyzing the Phenotypes of Nonheading Chinese Cabbage Leaves Based on Deep Learning and OpenCV Phenotype Extraction

Nonheading Chinese cabbage is an important leafy vegetable, and quantitative identification and automated analysis of nonheading Chinese cabbage leaves are crucial for cultivating new varieties with higher quality, yield, and resistance. Traditional leaf phenotypic analysis relies mainly on phenotypic observation and the practical experience of breeders, leading to issues such as time consumption, labor intensity, and low precision, which result in low breeding efficiency. Considering these issues, a method for the extraction and analysis of phenotypes of nonheading Chinese cabbage leaves is proposed, targeting four qualitative traits and ten quantitative traits from 1500 samples, by integrating deep learning and OpenCV image processing technology. First, a leaf classification model is trained using YOLOv8 to infer the qualitative traits of the leaves, followed by the extraction and calculation of the quantitative traits of the leaves using OpenCV image processing technology. The results indicate that the model achieved an average accuracy of 95.25%, an average precision of 96.09%, an average recall rate of 96.31%, and an average F1 score of 0.9620 for the four qualitative traits. From the ten quantitative traits, the OpenCV-calculated values for the whole leaf length, leaf width, and total leaf area were compared with manually measured values, showing RMSEs of 0.19 cm, 0.1762 cm, and 0.2161 cm2, respectively. Bland–Altman analysis indicated that the error values were all within the 95% confidence intervals, and the average detection time per image was 269 ms. This method achieved good results in the extraction of phenotypic traits from nonheading Chinese cabbage leaves, significantly reducing the personpower and time costs associated with genetic resource analysis. This approach provides a new technique for the analysis of nonheading Chinese cabbage genetic resources that is high-throughput, precise, and automated.

Application of sensor recognition based on artificial intelligence image algorithms in sports and human health

With the rapid development of artificial intelligence technology, the application of image algorithm in multimedia vision technology has made remarkable progress. In the field of sports, human health is an important concern, so it is of great significance to apply artificial intelligence image algorithm to the research of human health in sports. This paper uses computer vision and image processing technology, combined with artificial intelligence algorithm, to analyze and process the multimedia image of the movement. Through the acquisition and preprocessing of moving images, the key information and features of the movement are extracted. Then, deep learning algorithms and pattern recognition technology are used to analyze and evaluate the posture, movement and body state of the exercisers. Finally, according to the analysis results, personalized health advice and guidance are provided. The results show that the method can accurately identify and analyze the posture, movement and body state of the athletes, provide personalized health advice and guidance, help the athletes to improve the training effect, avoid sports injuries, and improve the level and quality of sports.

Real-Time Dynamic Intelligent Image Recognition and Tracking System for Rockfall Disasters.

Taiwan, frequently affected by extreme weather causing phenomena such as earthquakes and typhoons, faces a high incidence of rockfall disasters due to its largely mountainous terrain. These disasters have led to numerous casualties, government compensation cases, and significant transportation safety impacts. According to the National Science and Technology Center for Disaster Reduction records from 2010 to 2022, 421 out of 866 soil and rock disasters occurred in eastern Taiwan, causing traffic disruptions due to rockfalls. Since traditional sensors of disaster detectors only record changes after a rockfall, there is no system in place to detect rockfalls as they occur. To combat this, a rockfall detection and tracking system using deep learning and image processing technology was developed. This system includes a real-time image tracking and recognition system that integrates YOLO and image processing technology. It was trained on a self-collected dataset of 2490 high-resolution RGB images. The system's performance was evaluated on 30 videos featuring various rockfall scenarios. It achieved a mean Average Precision (mAP50) of 0.845 and mAP50-95 of 0.41, with a processing time of 125 ms. Tested on advanced hardware, the system proves effective in quickly tracking and identifying hazardous rockfalls, offering a significant advancement in disaster management and prevention.

Mechanism of asphalt concrete reinforced with industrially recycled steel slag from the perspectives of adhesion and skeleton

This research investigated the reinforcement mechanism of steel slag recycling in asphalt concrete from the perspective of adhesion and skeleton. Bonding performance between steel slag and bitumen was assessed by the calculation of coating area and peeling area, as well as the area detection by Scanning Electron Microscope (SEM) and Energy Dispersive Spectrometer (EDS). Computed Tomography (CT) scanning was used to determine the bitumen absorption characteristics of steel slag. Mechanical characteristics of steel slag mixture during compaction period and service period were evaluated by continuous compression test with Universal Testing Machine (UTM-25). A combination of CT scanning and image processing technology were used to quantify skeleton contact characteristics. The results indicated that bonding performance of bitumen with steel slag was obviously better than basalt after long-term erosion. The surface pores of steel slag absorbed excessive bitumen as anchoring and embedding effect, accounting for 20.24% of the total steel slag bitumen absorption in maximum. Steel slag skeleton of SMA gradation was found easily to be compacted during construction period and more resistant to loading during service period. In addition, there were more contact points in steel slag asphalt concrete, resulting that steel slag asphalt concrete had better stability. The contact points were different in different particle size ranges, and it was suggested that steel slag with particle size of 9.5–16.0 mm and 2.36–4.75 mm should be preferred.

Super-resolution reconstruction of 3D digital rocks by deep neural networks

Digital rock technology provides valuable insights into the pore structure and fluid flow properties of geoenergy resources. Artificial intelligence technology in vision and image processing, especially the image super-resolution, has great potential for digital rock reconstruction and resolution enhancement. However, the analyzed core samples are typically sandstones/carbonates in micro-scale resolutions and in two-dimensional (2D) space, whereas the shale rocks in nano-scale resolutions for unconventional resources or three-dimensional (3D) digital cores are rarely investigated. Additionally, previous studies primarily emphasized image quality from a computer vision perspective, with little consideration given to estimating physical properties of digital rocks using super-resolution techniques.This study presents a very deep super-resolution (VDSR) algorithm, specifically designed to generate high-resolution 3D digital rock images, for nano-scale shale matrix and micro-scale hydraulic fractures. We compare both image quality and permeability accuracy between the original high-resolution images and the super-resolution images reconstructed by the proposed method. The results reveal that the reconstructed images using the proposed method closely resemble the actual images, and effectively reduce errors in permeability computations. This study highlights the applicability of the proposed VDSR algorithm in establishing the detailed structures of 3D nano-scale shale matrix and hydraulic fractured rocks, thus advancing super-resolution techniques in digital core analysis for geoenergy resources development.

Neural network big data fusion in remote sensing image processing technology

Abstract Remote sensing (RS) image processing has made significant progress in the past few years, but it still faces some problems such as the difficulty in processing large-scale RS image data, difficulty in recognizing complex background, and low accuracy and efficiency of processing. In order to improve the existing problems in RS image processing, this study dealt with ConvNext-convolutional neural network (CNN) and big data (BD) in parallel. Moreover, it combined the existing RS image processing with the high dimensional analysis of data and other technologies. In this process, the parallel processing of large data and high-dimensional data analysis technology improves the difficulty and low efficiency of large-scale RS image data processing in the preprocessing stage. The ConvNext-CNN optimizes the two modules of feature extraction and object detection in RS image processing, which improves the difficult problem of complex background recognition and improves the accuracy of RS image processing. At the same time, the performance of RS image processing technology after neural networks (NNs) and BD fusion and traditional RS image processing technology in many aspects are analyzed by experiments. In this study, traditional RS image processing and RS image processing combined with NN and BD were used to process 2,328 sample datasets. The image processing accuracy and recall rate of traditional RS image processing technology were 81 and 82%, respectively, and the F1 score was about 0.81 (F1 value is the reconciled average of accuracy and recall, a metric that combines accuracy and recall to evaluate the quality of the results, a higher F1 value indicates a better overall performance of the retrieval system). The accuracy rate and recall rate of RS image processing technology, which integrates NN and BD, were 97 and 98%, respectively, and its F1 score was about 0.97. After analyzing the process of these experiments and the final output results, it can be determined that the RS image processing technology combined with NN and BD can improve the problems of large-scale data processing difficulty, recognition difficulty under complex background, low processing accuracy and efficiency. In this study, the RS image processing technology combined with NN and BD has stronger adaptability with the help of NN and BD technology, and can adjust parameters and can be applied in more tasks.

Digital image processing: A new tool for morphological measurements of freshwater turtles under rehabilitation.

Freshwater fauna is facing an uphill task for survival in the Ganga Basin, India, due to a range of factors causing habitat degradation and fragmentation, necessitating conservation interventions. As part of the ongoing efforts to conserve the freshwater fauna of the Basin, we are working on rehabilitating rescued freshwater chelonians. We carry out various interventions to restore rescued individuals to an apparent state of fitness for their release in suitable natural habitats. Morphometric measurements are crucial to managing captive wild animals for assessing their growth and well-being. Measurements are made using manual methods like vernier caliper that are prone to observer error experience and require handling the specimens for extended periods. Digital imaging technology is rapidly progressing at a fast pace and with the advancement of technology. We acquired images of turtles using smartphones along with manual morphometric measurements using vernier calipers of the straight carapace length and straight carapace width. The images were subsequently processed using ImageJ, a freeware and compared with manual morphometric measurements. A significant decrease in the time spent in carrying out morphometric measurements was observed in our study. The difference in error in measurements was, however, not significant. A probable cause for this may have been the extensive experience of the personnel carrying out the measurements using vernier caliper. Digital image processing technology can cause a significant reduction in the stress of the animals exposed to handling during measurements, thereby improving their welfare. Additionally, this can be used in the field to carry out morphometric measurements of free-ranging individuals, where it is often difficult to capture individuals, and challenges are faced in obtaining permission to capture specimens.

Advancements in Utilizing Image-Analysis Technology for Crop-Yield Estimation

Yield calculation is an important link in modern precision agriculture that is an effective means to improve breeding efficiency and to adjust planting and marketing plans. With the continuous progress of artificial intelligence and sensing technology, yield-calculation schemes based on image-processing technology have many advantages such as high accuracy, low cost, and non-destructive calculation, and they have been favored by a large number of researchers. This article reviews the research progress of crop-yield calculation based on remote sensing images and visible light images, describes the technical characteristics and applicable objects of different schemes, and focuses on detailed explanations of data acquisition, independent variable screening, algorithm selection, and optimization. Common issues are also discussed and summarized. Finally, solutions are proposed for the main problems that have arisen so far, and future research directions are predicted, with the aim of achieving more progress and wider popularization of yield-calculation solutions based on image technology.

Research on Coaxiality Measurement Method for Automobile Brake Piston Components Based on Machine Vision

Aiming at addressing the problem of the online detection of automobile brake piston components, a non-contact measurement method based on the combination of machine vision and image processing technology is proposed. Firstly, an industrial camera is used to capture an image, and a series of image preprocessing algorithms is used to extract a clear contour of a test piece with a unit pixel width. Secondly, based on the structural characteristics of automobile brake piston components, the region of interest is extracted, and the test piece is segmented into spring region and cylinder region. Then, based on mathematical morphology techniques, the edges of the image are optimized. We extract geometric feature points by comparing the heights of adjacent pixel points on both sides of the pixel points, so as to calculate the variation of the spring axis relative to the reference axis (centerline of the cylinder). Then, we extract the maximum variation from all images, and calculate the coaxiality error value using this maximum variation. Finally, we validate the feasibility of the proposed method and the stability of extracting geometric feature points through experiments. The experiments demonstrate the feasibility of the method in engineering practice, with the stability in extracting geometric feature points reaching 99.25%. Additionally, this method offers a new approach and perspective for coaxiality measurement of stepped shaft parts.

Applications of Imaging Technologies in Fuchs Endothelial Corneal Dystrophy: A Narrative Literature Review.

Fuchs endothelial corneal dystrophy (FECD) is a complex genetic disorder characterized by the slow and progressive degeneration of corneal endothelial cells. Thus, it may result in corneal endothelial decompensation and irreversible corneal edema. Moreover, FECD is associated with alterations in all corneal layers, such as thickening of the Descemet membrane, stromal scarring, subepithelial fibrosis, and the formation of epithelial bullae. Hence, anterior segment imaging devices that enable precise measurement of functional and anatomical changes in the cornea are essential for the management of FECD. In this review, the authors will introduce studies on the application of various imaging modalities, such as anterior segment optical coherence tomography, Scheimpflug corneal tomography, specular microscopy, in vitro confocal microscopy, and retroillumination photography, in the diagnosis and monitoring of FECD and discuss the results of these studies. The application of novel technologies, including image processing technology and artificial intelligence, that are expected to further enhance the accuracy, precision, and speed of the imaging technologies will also be discussed.

Automatic crimping state classification in X-ray images of crimped tension clamp

ABSTRACT The crimping states of the tension clamp can typically be recognised by using X-ray radiography. However, it is challenging to process X-ray images directly for inspection, especially when the crimping state and position of the groove are unclear. To address this problem, this paper firstly adopted the Beta greyscale stretching algorithm to enhance the X-ray images by increasing the contrast and reducing the noise. Subsequently, the Canny algorithm was implemented to locate and segment the groove region. Finally, the Grey-Level Co-occurrence Matrix (GLCM) method was applied to extract the texture features of the groove region, and identify the groove crimping state using support vector machine (SVM) classifier. Three types of the crimping states were utilised as test objects for classification. The results of the experiments demonstrated that the Beta enhancement algorithm, GLCM features used in this paper can effectively improve the accuracy rate of SVM classifier up to 99.51%. This study applies image processing technology towards the crimping states classification of tension clamp with accurate detection results enabling early-stage detection of abnormal crimping states leading to timely repairs.

Depth_ASPP_UNet++: a novel 2D ESPI phase unwrapping based on convolutional neural network

Phase unwrapping is a key step in optical metrology and physical optics to obtain accurate phase distributions. In practice, phase images obtained from electronic speckle pattern interferometry (ESPI) exhibit diverse and complex morphology, with significant shape variations and non-uniform densities among different individuals. This takes challenges for accurately extracting phase information and unwrapping the phase. With the progress of deep learning technology in optical image processing, real-time performance and accuracy have become concerned issues. In this paper, an ESPI phase unwrapping method based on convolutional neural network UNet++ is proposed. The proposed network combines the depthwise separable convolution (DSC), atrous spatial pyramid pooling (ASPP), defined as Depth_ASPP_UNet++. In this model, the use of DSC improves network computational efficiency and provides better feature representation capability. In addition, ASPP is introduced to pay more attention to the phase information of the phase image, and then obtain better phase unwrapping results. The experimental results show that our proposed method can obtain excellent results, especially with various of variable density, different noise levels, and different speckle sizes.

Dielectric Liquid Microlens Array with Tunable Focal Length Based on Microdroplet Array Created via Dip-Coating Method.

A dielectric liquid microlens array (LMA) with a tunable focal length was fabricated by using a microdroplet array generated through the dip-coating method. The process began with treating the octadecyltrichlorosilane (OTS) layer with selective UV/O3 irradiation for 20 min to establish a hydrophilic-hydrophobic patterning surface. The substrate was subsequently immersed in glycerol and then withdrawn at a constant rate to create a microdroplet array. Upon filling the cell with matching oil (SL5267) and placing it within a square array of a 200 μm diameter glycerol microdroplet array, the LMA was produced. The focal length ranged from approximately -0.96 to -0.3 mm within a voltage range of 0 to 60 Vrms. The glycerol microdroplets, characterized by their shapes, sizes, curvatures, and filling factors, can be precisely controlled by designing an OTS patterning or adjusting the dip-coating speed. This approach offers a rapid and high-throughput method for preparation. Our approach to fabricating tunable LMA offers several advantages, including simplicity of fabrication, uniform structural properties, cost-effectiveness, polarization independence, and excellent optical performance. These focus-tunable LMAs hold significant potential for applications in image processing, 3D displays, medical endoscopy, and military technologies.

Characterization and Detection Classification of Moldy Corn Kernels Based on X-CT and Deep Learning

Moldy corn produces aflatoxin and gibberellin, which can have adverse effects on human health if consumed. Mold is a significant factor that affects the safe storage of corn. If not detected and controlled in a timely manner, it will result in substantial food losses. Understanding the infection patterns of mold on corn kernels and the changing characteristics of the internal structure of corn kernels after infection is crucial for guiding innovation and optimizing detection methods for moldy corn. This knowledge also helps maintain corn storage and ensure food safety. This study was based on X-ray tomography technology to non-destructively detect changes in the structural characteristics of moldy corn kernels. It used image processing technology and model reconstruction algorithms to obtain the 3D model of the embryo, pores and cracks, endosperm and seed coat, and kernels of moldy corn kernels; qualitative analysis of the characteristic changes of two-dimensional slice grayscale images and 3D models of moldy corn kernels; and quantitative analysis of changes in the volume parameters of corn kernels, embryos, endosperm, and seed coats as a whole. It explored the detection method of moldy corn kernels based on a combination of X-ray tomography technology and deep learning algorithms. The analysis concluded that mold infection in maize begins in the embryo and gradually spreads and that mold damage to the tissue structure of maize kernels is irregular in nature. The overall volume parameter changes of corn kernels, embryos, endosperm, and seed coats in the four stages of 0 d, 5 d, 10 d, and 15 d showed a trend of first increasing and then decreasing. The ResNet50 model was enhanced for detecting mold on maize kernels, achieving an accuracy of over 93% in identifying mold features in sliced images of maize kernels. This advancement enabled the non-destructive detection and classification of the degree of mold in maize kernel samples. This article studies the characterization of the characteristic changes of moldy corn kernels and the detection of mildew, which will provide certain help for optimizing the monitoring of corn kernel mildew and the development of rapid detection equipment.

FruitScan Pro : Fruit Disease and Quality Detection Application

This research paper proposes a revolutionary approach to agriculture by combining the latest image processing technologies with machine learning. The agricultural industry, which is vital for India’s economy, needs efficient methods for monitoring fruit health. Manual monitoring is time consuming and often inadequate. In this paper, we propose a smart farming approach using image processing techniques. This approach improves image quality, segmenting images, extracting features, and classifying diseases and their cures. Fruit diseases are a major economic challenge around the world. Therefore, it is necessary to identify them quickly. Our proposed system offers fast and automatic disease detection. Preprocessing, extracting features, classifying, training/testing. The results prove its effectiveness for accurate disease detection and solutions. As the demand for agricultural productivity increases, this method will be a valuable tool for increasing fruit production with minimum human intervention. Image processing for effective implementation. Key Words: Fruit Disease, Solution, Image Preprocessing, Feature Extraction, Classification.

Design and evaluate the performance of a mechanical system for the release of Harmonia axyridis adults.

Harmonia axyridis (H. axyridis) is the natural enemy of many aphid species. Traditional manual release of H. axyridis adults requires substantial manpower, and release efficiency is low. Automatic mechanical devices can improve the efficiency of delivery. Based on H. axyridis adults' morphological size, a prototype release system for H. axyridis was designed, which considered the adhesion characteristics of H. axyridis adults. According to the measured physical characteristics of H. axyridis adults, the structural parameters of the mechanical system for the release of the H. axyridis adults were determined. The relationship of the quantity of release, the impeller rotating speed, and the time for the release of H. axyridis adults were constructed. The mechanism can quantitatively adjust the number of H. axyridis adults to meet a certain H. axyridis-aphids ratio. Combining the image processing technology with the camera function of a mobile phone, the maximum cross-sectional area method was used to count the H. axyridis adults in the designated area. Results showed that the impeller rotating speed had a significant effect on the survival rate of the H. axyridis adults. When the airflow velocities were 29.5 m/s and 38.3 m/s, the survival rates of the H. axyridis adults were 93.8% and 94.5% at 4.2 rpm. The adhesion rate of the H. axyridis adults was 2.5%-4.6%. This work will provide technical support for the research of biological control.

Numerical simulation and experimental investigation of lattice-apron compact spinning with an air guide

The four-roller compact spinning system is widely used today and is considered an improvement over traditional ring spinning because of its excellent yarn properties. Reducing energy consumption in compact spinning has always been a key issue for the textile industry. This paper proposed a method for installing an air guide on compact spinning to reduce energy consumption. Four types of air guides were designed with different side shapes (Type-N, Type-L, Type-R, and Type-LR), and the airflow characteristics in the converging zone were analyzed using numerical simulation. In addition, high-speed photography technology and image-processing technology were used to study the dynamic state of the fiber bundle in the flow field of the converging zone. The effectiveness of the preferred solution of the air guide was also verified by spinning experiments. The results revealed that the best air guide was Type-LR, achieving a 3.7% reduction in yarn hairiness even after a decrease in negative pressure by 800 Pa and a 58.4% reduction in power of the fan motor, surpassing the yarn quality observed without any air guide.

MACHINE LEARNING AND BLOCKCHAIN-BASED REAL-TIME FACIAL RECOGNITION ATTENDANCE SYSTEM

In a vast majority of fields, the use of facial recognition for authentication is expanding. In this information age, authentication has become vital, and the need for faster and more secure methods of user authentication has been on the rise. The introduction of image processing technologies such as OpenCV has increased society’s reliance on face recognition. Using blockchain, information could be stored in blocks throughout the blockchain network. Blockchain is an extremely secure means for storing and protecting data from intruders. It is a highly disruptive technology that has the ability to alter every plane of society. This paper intends to implement opensource computer vision (OpenCV) to construct a facial detection model that will be employed in a blockchain-secured Attendance Monitoring System. It will not only automate the attendance procedure but also give the system unassailable security. This system will take a live video feed from a camera using OpenCV and identify the faces of students and record their attendance along with the entry time. The data will be kept in a distributed way over the blockchain network that will be accessible to everyone, but data cannot be manipulated.