Image Processing Technology Research Articles

Design of a Crossroad Traffic Light System

Nowadays, the situation of traffic jams is gradually increasing, and the demand for intelligent traffic lights is increasing as well. This paper designs a crossroad traffic light based on sequential logic using NI Multisim 14.0 virtual simulation software. The design includes: using 555 timer to build a second pulse oscillator circuit which outputs clock signal. Using 74LS169 counting chip to build timing circuit. Set up state control with 74LS74D trigger. Using nixie tubes and 74LS47 decoder chips to build the timing display circuit. Finally, logic gate and LED lights are used to build the display circuit of traffic lights. Next, how to design intelligent traffic lights is discussed based on this design combined with image monitoring technology. The feedback bit of image processing technology is added to the designed circuit, so that the circuit can change the green light duration with the traffic flow, and the designed circuits are also becoming smarter.

Experimental and statistical study on sound absorption coefficient of porous asphalt concrete considering mesoscopic pore parameters

Porous asphalt concrete (PAC) has attracted significant attention in the road industry due to its excellent noise reduction capabilities. However, clogging in PAC decreases its sound absorption performance, as mesoscopic pore parameters play a crucial role in influencing PAC’s sound absorption abilities. To further investigate the influence of mesoscopic pore parameters on the sound absorption performance of PAC, CT scanning and digital image processing technology were employed. The principal component analysis method was employed to determine the weighting of the influence of mesoscopic pore parameters on the sound absorption coefficient. The correlation between different pore parameters and the sound absorption coefficient of PAC specimens was analyzed. The results showed that the minimum Feret diameter has the most significant effect on the sound absorption performance of PAC, while the concavity and the length-short axis ratio have the least impact. The correlation between mesoscopic pore parameters and sound absorption coefficients was found to be inconsistent before and after clogging. Mesoscopic pore parameters that were positively correlated with sound absorption coefficients before clogging turned negatively correlated after clogging. Connected large pores and smooth regular pore structure enhance the sound absorption performance of PAC, but clogging disrupts this structure, resulting in a decrease in sound absorption performance.

Enhancing Automatic Vehicle Number Plate Recognition Systems: A Multidimensional Approach to Improve Efficiency and Applicability

Real Time Number Plate Recognition System is an image processing technology which uses number (license) plate to identify the vehicle. The objective is to design an efficient automatic authorized vehicle identification system by using the vehicle number plate. No plate help for movement of traffic securely without any collision. They can reduce the number of accidents on roads like pedestrian accident and right-angle collision of two cars. Number plate recognition (NPR) can be used in various fields such as vehicle tracking, traffic monitoring, automatic payment of tolls on highways or bridges, surveillance systems, tolls collection points, and parking management systems. The developed system first detects the vehicle and then captures the vehicle image. Vehicle number plate region is localized using Neural Network(rcnn) then image segmentation is done on the image. Character recognition technique is used for the character extraction from the plate. The system is implemented and simulated in python, and its performance is tested on real image. Automatic vehicle detection and recognition is a key technique in most of traffic related applications and is an active research topic in the image processing domain. Different methods, techniques and algorithms have been developed for vehicle detection and recognitions but they are not very useful for parking system. In this project we aim to make an application which will help for society and mostly for corporation buldings in each state for doing their work very efficiently and in very small time. Live camera feeds are the primary input we give the system. On the main gate is a camera. When a car or bike passes in front of the camera, the camera reads the number plate number. Using an OCR algorithm, this vehicle can be determined to be authorized or not. If the car is not allowed in the premises the notification will be activated. authorized popup will be on if the vehicle is authorized

Big data analytics for image processing and computer vision technologies in sports health management.

Visualization of sports has a lot of potential for future development in data sports because of how quickly things are changing and how much sports depend on data. Presently, conventional systems fail to accurately address sports persons' dynamic health data change with less error rate. Further, those systems are unable to distinguish players' health data and their visualization in a precise manner. An excellent starting point for building fitness solutions based on computer vision technology is the data visualization technology that arose in the age of big data analytics. This research presents a Big Data Analytic assisted Computer Vision Model (BD-CVM) for effective sports persons healthcare data management with improved accuracy and precision. The fitness and health of professional athletes are analyzed using information from a publiclyavailable sports visualization dataset. Machine learning-assisted computer vision dynamic algorithm has been used for an effective image featuring and classification by categorizing sports videos through temporal and geographical data. The significance of big data's great potential in screening data during a sporting event can be reasonably analyzed and processed effectively with less error rate. The proposed BD-CVM utilized an error analysis module which can be embedded in the design further to ensure the accuracy requirements in the data processing from sports videos. The research findings of this paper demonstrate that the strategy presented here can potentially improve accuracy and precision and optimize mean square error in sports data classification and visualization.

Application of Computer Image Processing Technology in Intelligent Monitoring System

With the continuous progress of science and technology and the rapid development of society, intelligent surveillance systems are gradually emerging in the field of security and other related fields, and have become a key tool for ensuring public safety and improving efficiency. Intelligent monitoring system with computer vision and image processing technology as the core, with the help of advanced hardware equipment and intelligent algorithms, can be efficiently processed and deeply analysed image information in real-time monitoring. This paper provides a strong support to build a safer and more efficient society by studying in depth the application of image processing technology in intelligent surveillance system with the goal of comprehensively promoting the technological innovation of intelligent surveillance system.

Performance Evaluation of Some Selected Classification Algorithms in a Facial Recognition System

Facial Recognition (FR) has been an active area of research and has diverse applicable environment, it continues to be a challenging research topic. With the development of image processing and pattern recognition technology, there are many challenges in machine learning to select the appropriate classification algorithms, most especially in the area of classification of extracted features to have low classification time, high sensitivity and accuracy of the classification algorithms, so it is very important to explore the performance of different algorithms in image classification. The three selected supervised learning classification algorithms: Learning Vector Quantization (LVQ), Relevance Vector Machine (RVM), and Support Vector Machine (SVM) performance were evaluated so as to know the most effective out of the selected algorithms for facial images classification. The development of the system has four stages, the first stage is image acquisition and 180 images were taken by digital camera under same illumination and light colour background. The second stage is pre-processing to improve the images data by suppressing unwilling distortion; grayscale and normalization were used for image pre-processing. The third stage is feature extraction; Discrete Cosine Transform (DCT) is adopted for this purpose. While the fourth stage is face recognition classification, Receiver Operating Characteristics (ROC) was used to test the performance of each the three algorithms. However the Learning Vector Quantization algorithm, Relevance Vector Machine and Support Vector Machine performance have not been compared together to the most effective out of the three algorithms in term of False Positive Rate, Sensitivity, Specificity, Precision, Accuracy and Computation Time. Hence, this work evaluated the performance of the Learning Vector Quantization; Relevance Vector Machine and Support Vector Machine classification algorithms in facial recognition system and Support Vector Machine outwit the other two algorithms in facial recognition in term of specificity, recognition time and recognition accuracy at different threshold.

Intelligent robotic control system based on computer vision technology

Computer vision is a kind of simulation of biological vision using computers and related equipment. It is an important part of the field of artificial intelligence. Its research goal is to make computers have the ability to recognize three-dimensional environmental information through two-dimensional images. Computer vision is based on image processing technology, signal processing technology, probability statistical analysis, computational geometry, neural network, machine learning theory and computer information processing technology, through computer analysis and processing of visual information.The article explores the intersection of computer vision technology and robotic control, highlighting its importance in various fields such as industrial automation, healthcare, and environmental protection. Computer vision technology, which simulates human visual observation, plays a crucial role in enabling robots to perceive and understand their surroundings, leading to advancements in tasks like autonomous navigation, object recognition, and waste management. By integrating computer vision with robot control, robots gain the ability to interact intelligently with their environment, improving efficiency, quality, and environmental sustainability. The article also discusses methodologies for developing intelligent garbage sorting robots, emphasizing the application of computer vision image recognition, feature extraction, and reinforcement learning techniques. Overall, the integration of computer vision technology with robot control holds promise for enhancing human-computer interaction, intelligent manufacturing, and environmental protection efforts.

Application of Computer Image Processing and Recognition Technology

Application of Computer Image Processing and Recognition Technology

Permeability evolution of fractured coal subject to confining stress and true triaxial stress loading: experiment and mathematical model

The accurate elucidation and prediction of coal permeability evolution under stress loading conditions are crucial for coalbed methane production. In this study, flow experiments were conducted on six cylindrical coal samples and four cubic coal samples under both confining and true triaxial stress loading conditions, respectively. The structure and characteristic parameters of the fractures inside each coal sample were obtained using the computed tomography scanning system and image processing technologies. The coal permeability under both types of loading processes was calculated through the transient pulse method. A mathematical model was developed to assess the evolution of coal permeability under true triaxial loading based on the current true triaxial permeability model and fractal theory. The results revealed that during the confining pressure loading, the coal permeability decreased exponentially with effective stress and was effectively described using the SD model. Additionally, the coal permeability initially rapidly decreased, followed by a gradual decrease, and eventually stabilized at a constant value. Particularly, during the first three loading steps, the fracture aperture and corresponding permeability of the six cylindrical coal samples decreased by ∼51.79%–57.83% and ∼38.06%–42.12%, respectively. However, during the final three loading steps, the fracture aperture and corresponding permeability of the six coal samples decreased by ∼18.26%–23.08% and ∼22.15%–26.93%, respectively. Moreover, owing to the various crossing angles of complex fracture networks with each principal stress, the effect of each principal stress on the coal permeability evolution was highly anisotropic during triaxial stress loading. Particularly, the permeability of the ST1 sample decreased by 43.08%, 14.84%, and 42.08% during the loading of each principal stress. Similarly, the permeability of the ST2 sample decreased by ∼65.74%, 14.29%, and 19.97%. The permeability reductions for the ST3 sample were ∼34.03%, 55.85%, and 10.12%, while those for the ST4 sample were ∼35.97%, 46.51%, and 17.52%. The SD model failed to describe these anisotropic effects. Compared with the SD model, the improved model, based on the current true triaxial permeability model and fractal theory, effectively described the anisotropic effect of each principal stress on the permeability of coal samples with complex fracture networks under triaxial stress conditions.

Staging of Liver Fibrosis Based on Energy Valley Optimization Multiple Stacking (EVO-MS) Model.

Currently, staging the degree of liver fibrosis predominantly relies on liver biopsy, a method fraught with potential risks, such as bleeding and infection. With the rapid development of medical imaging devices, quantification of liver fibrosis through image processing technology has become feasible. Stacking technology is one of the effective ensemble techniques for potential usage, but precise tuning to find the optimal configuration manually is challenging. Therefore, this paper proposes a novel EVO-MS model-a multiple stacking ensemble learning model optimized by the energy valley optimization (EVO) algorithm to select most informatic features for fibrosis quantification. Liver contours are profiled from 415 biopsied proven CT cases, from which 10 shape features are calculated and inputted into a Support Vector Machine (SVM) classifier to generate the accurate predictions, then the EVO algorithm is applied to find the optimal parameter combination to fuse six base models: K-Nearest Neighbors (KNNs), Decision Tree (DT), Naive Bayes (NB), Extreme Gradient Boosting (XGB), Gradient Boosting Decision Tree (GBDT), and Random Forest (RF), to create a well-performing ensemble model. Experimental results indicate that selecting 3-5 feature parameters yields satisfactory results in classification, with features such as the contour roundness non-uniformity (Rmax), maximum peak height of contour (Rp), and maximum valley depth of contour (Rm) significantly influencing classification accuracy. The improved EVO algorithm, combined with a multiple stacking model, achieves an accuracy of 0.864, a precision of 0.813, a sensitivity of 0.912, a specificity of 0.824, and an F1-score of 0.860, which demonstrates the effectiveness of our EVO-MS model in staging the degree of liver fibrosis.

Visual analysis of sea buckthorn fruit moisture content based on deep image processing technology

The effect of moisture content changes during drying processing on the appearance of sea buckthorn was studied. Using computer vision methods and various image processing methods to collect and analyze images during the drying process of sea buckthorn fruit. Sea buckthorn is dried in a drying oven at a temperature of 65 °C and Level 1 wind speed conditions. The images of the entire drying process of sea buckthorn fruit were collected at 30-min intervals. Deep mining and transformation of image information through various image processing methods. By calibrating and modeling the color components, real-time online detection of the moisture content of sea buckthorn fruit can be achieved. After modeling, this article attempted to use LSTM (Long Short Term Memory) to predict the appearance of sea buckthorn fruit with supercritical moisture content. Different agricultural products adapt to different color spaces, but after standard modeling with a certain amount of data, applying color components to detect moisture content is a very good method.

Using Artificial Intelligence to Refine the Implementation Trajectory of Digital Image Processing Technology

Artificial intelligence introduces a fresh research perspective to digital image processing. However, its integration into the curriculum of colleges and universities for teaching digital image processing remains scarce. This lack of incorporation results in outdated course content, reliance on singular teaching methods, and simplistic course experiments, consequently impeding effective teaching and hindering the development of well-rounded and innovative individuals. Digital image processing technology expands the horizons of communication engineering, facilitating more convenient modes of communication for people. For instance, video calls and photo transmissions diversify everyday communication methods, transcending the constraints of time and space by enabling online meetings and fostering enhanced communication possibilities. Despite these advancements, numerous challenges and methodologies merit thorough exploration. Therefore, this paper aims to comprehensively grasp both traditional and deep learning approaches to digital image processing, enhancing practical project proficiency and fostering scientific research exploration capabilities, thus serving as a valuable reference for similar research endeavors.

Gait Analysis of Knee Joint Walking Based on Image Processing.

With the in-depth development of assistive treatment devices, the application of artificial knee joints in the rehabilitation of amputees is becoming increasingly mature. The length of residual limbs and muscle strength of patients have individual differences, and the current artificial knee joint lacks certain adaptability in the personalized rehabilitation of patients. In order to deeply analyze the impact of different types of artificial knee joints on the walking function of unilateral thigh amputees, improve the performance of artificial knee joints, and enhance the rehabilitation effect of patients, this article combines image processing technology to conduct in-depth research on the walking gait analysis of different artificial knee joints of unilateral thigh amputees. This article divides patients into two groups: the experimental group consists of patients with single leg amputation, and the control group consists of patients with different prostheses. An image processing system is constructed using universal video and computer hardware, and relevant technologies are used to recognize and track landmarks; Furthermore, image processing technology was used to analyze the gait of different groups of patients. Finally, by analyzing the different psychological reactions of amputees, corresponding treatment plans were developed. Different prostheses worn by amputees have brought varying degrees of convenience to life to a certain extent. The walking stability of wearing hydraulic single axis prosthetic joints is only 79%, and the gait elegance is relatively low. The walking stability of wearing intelligent artificial joints is as high as 96%. Elegant gait is basically in good condition. Image processing technology helps doctors and rehabilitation practitioners better understand the gait characteristics and rehabilitation progress of patients wearing different artificial knee joints, providing objective basis for personalized rehabilitation of patients.

Smartphone-based straw incorporation: An improved convolutional neural network

In conservation tillage, the effective incorporation of straw into soil is a critical factor for optimizing decomposition rates and addressing challenges related to straw accumulation. Consequently, this study introduces an innovative straw semantic segmentation model to extract straw mixed with soil from images, and proposes a method to measure the quality of straw and soil mixing based on this model. We conducted field experiments based on this method, analyzed the quality of straw incorporation at different depths, and finally integrated the model into a mobile application to facilitate the automatic, accurate, and rapid assessment of the quality of straw and soil mixing. The methodology of this study as an improved convolutional neural network entailed the fusion of deep separable convolution with a multi-scale feature extraction module, utilizing convolution kernels of varying sizes to capture the diverse features of straw. Further refinement included halving the first convolution channel and implementing a direct connection structure. Attention gates were strategically deployed to enhance salient features, resulting in superior accuracy compared to the original U-shaped network, fully convolutional network, and Otsu algorithm. Notably, the proposed model achieved enhanced performance while reducing the number of parameters and model complexity to one-third and one-half of the original U-shaped network, respectively. For a quantitative description of the rate and uniformity of straw incorporation, this study employed image processing technology and a grid counting method. The synergistic integration of smartphone imagery and deep learning expedited the delivery of rapid and reliable results, promising practical applicability in future straw incorporation practices. A demonstration video of the application is accessible at: https://doi.org/10.6084/m9.figshare.14480745.v3. The findings of this research contribute significantly to the advancement of conservation tillage by establishing a robust framework for the assessment and enhancement of straw incorporation operations’ efficiency.

Effect of different photoperiod regimes in combination with natural and artificial light on nutrient uptake in bok choy (<em>Brassica rapa</em> L.) using an internet of things-based hydroponics system

In the present study, we analyzed the effect of using a hydroponic system inside a greenhouse and different photoperiod regimes with artificial light on the nutrient uptake of bok choy. Light duration treatment consisting of both sunlight and artificial light was applied to determine the optimal photoperiod for bok choy. Advanced technology—a wireless sensor network and Internet of Things—was used to monitor and maintain nutrient concentrations. Additionally, plant growth was evaluated using image processing technology. A higher amount of P was observed to be accumulated in plants grown in plots without photoperiod. Meanwhile, excessive photoperiod was found to reduce K content in plants. The optimal photoperiod in this study was 20:4 (light:dark), which is a combination of 12 h of sunlight and 8 h of artificial light. Additionally, image processing technology helped monitor plant growth. Pixel information in images can represent plant growth with a R2 value of >0.8. Further, the addition of photoperiod affects the dry weight of yields and growth rate, which is highly correlated to nutrient uptake, with R2 values of 0.84 and 0.72, respectively. The combination of artificial light and sunlight along with the optimal photoperiod can optimize the growth of bok choy with appropriate NPK uptake.

Weed Identification Using Deep Learning in Vegetable Farming

Weed identification in vegetable plantation is more challenging than crop weed identification due to their random plant spacing. So far, little work has been found on identifying weeds in vegetable plantation. Traditional methods of crop weed identification used to be mainly focused on identifying weed directly; however, there is a large variation in weed species. This paper proposes a new method in a contrary way, which combines deep learning and image processing technology. Firstly, a trained CenterNet model was used to detect vegetables and draw bounding boxes around them. Afterwards, the remaining green objects falling out of bounding boxes were considered as weeds. In this way, the models focus on identifying only the vegetables and thus avoid handling various weed species. Furthermore, this strategy can largely reduce the size of training image dataset as well as the complexity of weed detection, thereby enhancing the weed identification performance and accuracy. To extract weeds from the background, a colour index-based segmentation was performed utilizing image processing. The employed colour index was determined and evaluated through Genetic Algorithms (GAs) according to Bayesian classification error. During the field test, the trained CenterNet model achieved a precision of 95.6%, a recall of 95.0%, and a F1 score of 0.953, respectively. These experiment results demonstrate the feasibility of using the proposed method for the ground-based weed identification in vegetable plantation.

Detection and Counting of Fake Currency & Genuine Currency Using Image Processing

In today's digital age, the proliferation of counterfeit currency poses a significant challenge to financial institutions and businesses worldwide. The detection and prevention of counterfeit currency transactions require advanced technological solutions to safeguard the integrity of financial systems. In our research, we present a novel approach to tackle this problem by leveraging image processing techniques. The proposed system utilizes state-of-the-art image processing algorithms to distinguish between fake and genuine currency notes accurately. By analyzing various features such as texture, color, and watermark patterns, our system can effectively identify counterfeit currency notes with high precision. Additionally, the system is capable of counting and validating genuine currency notes, providing a comprehensive solution for currency authentication and management. The integration of image processing technology into currency verification systems offers numerous benefits, including enhanced accuracy, speed, and reliability. Our research aims to contribute to the development of robust and efficient solutions for combating counterfeit currency fraud, thereby safeguarding the integrity of financial transactions and promoting trust in monetary systems.

Fruit Sorting by Pizzo-electric Sensor and PLC Controlling

Abstract: The fruit sorting process traditionally relies on visual inspection, primarily considering size as a key quality parameter. However, industries engaged in large-scale fruit trading have turned to image processing technology for sorting, despite its high cost and impracticality for small traders. This paper proposes an alternative sorting system that offers an economical solution suitable for automated fruit sorting at various scales. Aim: The aim of this study is to develop an economical automated fruit sorting system capable of classifying fruits based on their weight, thereby eliminating the need for manual labor and improving sorting accuracy and efficiency. Place and Duration of Study: Department of Electrical Engineering Finolex Academy of Management and Technology, Ratnagiri, Maharashtra, between July 2023 to April 2024. Study Design: This study employs a practical approach to design an automated fruit sorting system that utilizes Programmable Logic Controller (PLC) technology in conjunction with load cells. The system is designed to classify fruits based on their weight as they move along a conveyor belt, thereby automating the sorting process. Methodology: The proposed sorting system integrates PLC technology and load cells to accurately measure and classify fruits based on their weight. By eliminating the need for manual intervention, the system enhances sorting efficiency and accuracy. The system is designed to be cost-effective and suitable for implementation by small traders and farmers. Results: The developed sorting system demonstrates precise, reliable, and consistent sorting outcomes based on fruit weight. By automating the sorting process, the system achieves improved efficiency and productivity compared to traditional manual sorting methods. Moreover, the cost-effectiveness of the system makes it accessible to small traders and farmers seeking to enhance their sorting practices. Conclusion: The proposed sorting system offers an economical and efficient solution for automated fruit sorting, particularly for small traders and farmers. By leveraging PLC technology and load cells, the system provides reliable alternative to costly image processing-based methods. The automation of the sorting process eliminates the risk of errors and inconsistencies associated with manual sorting, while ensuring accurate and quantitative classification based on fruit weight. This sorting system presents a practical approach to improving fruit trading operations, enhancing efficiency, and productivity.

Macro-meso properties and damage constitutive model of sandstone under chemical and freeze-thaw environments

The coupled effect of chemical corrosion and freeze-thaw cycle degrades the physical and mechanical properties of rock, affecting the safety and durability of underground rock mass engineering. This study investigated the deterioration laws of sandstone under pH = 1 and 3 chemical and freeze-thaw cycle action for different time considering macro and meso scales. The stress-strain curve of sandstone under uniaxial compression can be divided into compaction, elastic deformation, yield, and post-peak stages. The compaction of sandstone after chemical and freeze-thaw cycle action became apparent, elastic stage was relatively shortened, elastic modulus and peak strength decreased, and peak strain increased. The density and homogeneity of sandstone declined as per the CT scan and image processing technologies. The deterioration was prominent with longer action time and smaller pH value of the solution. After 120 days of pH = 1 and 3 chemical and freeze-thaw cycle action, the peak stress of samples decreased by 22.10% and 15.16%, the elastic modulus declined by 89.57% and 85.09%, the peak strain increased by 96% and 62%, and the damage variables reached 3.27% and 1.83%. The damage constitutive model of sandstone was established using statistical damage theories. The theoretical stress-strain curves were consistent with experimental ones. The insights of this research have implications for the theoretical foundation of safety assessments and disaster prevention in practical engineering scenarios under chemical and freeze-thaw environments.

Application of the Measurement Method of Building Surface Cracks Based on Image Processing Technology

Application of the Measurement Method of Building Surface Cracks Based on Image Processing Technology