Image Acquisition System Research Articles

Image acquisition based on computer vision technology for optimizing thermal energy in building environments and simulating VR interior design

With the acceleration of urbanization, the problem of building energy consumption has become increasingly prominent, and the construction of low energy consumption and high comfort building environment has become a hot research field. The purpose of this study is to explore the use of computer vision technology for image acquisition, optimize the thermal energy management in the building environment through intelligent analysis, and improve the energy efficiency and comfort of interior design. Through the establishment of image acquisition system based on computer vision, the indoor and outdoor environment is monitored in real time. A deep learning algorithm is used to process the acquired images and extract the thermal energy characteristics of the buildings. Combined with virtual reality (VR) technology, a 3D model of interior design was constructed to simulate the impact of different design schemes on energy consumption. The experimental results show that the real-time data obtained by computer vision technology can effectively identify the heat energy distribution in the building, and provide optimization suggestions for interior design. Simulation results of different design options show that the optimized design reduces energy consumption while improving occupant comfort. Therefore, computer vision technology shows a good application prospect in the thermal energy optimization of building environment. By combining VR interior design simulation, the technology not only improves the accuracy of thermal management, but also provides architects with more information-based decision support and promotes the development of smart buildings.

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.

Retraction Note: Image acquisition system based on optical sensors for swimmer technical analysis and simulation

Retraction Note: Image acquisition system based on optical sensors for swimmer technical analysis and simulation

Deep encoder-decoder networks for belt longitudinal tear detection

The belt conveyor is susceptible to longitudinal tearing, which poses a serious threat to the safety of coal mines. Traditional methods for detecting longitudinal tears have limitations such as poor image quality, limited applicability, and high hardware costs. An improved encoder-decoder network was proposed to solve the longitudinal tear detection problem. This method utilizes a line structured light system for image acquisition. The input images are downscaled using a sorting algorithm to extract the information of pixels with high grayscale values as the input feature map for the neural network. The reduced-dimensional encoder-decoder network then semantically segments the input feature map, and the resulting pixel segmentation is mapped to the location of the longitudinal tear. Finally, the position and length of the tear are calculated by back-projecting the semantic segmentation result to the world coordinate system. Experimental results demonstrate that this method effectively reduces hardware resource consumption and improves detection speed. The DICE and MIOU scores for the improved network are 97.69% and 95.47%, respectively, while the recall and precision for improved detection are 96.60% and 95.67%, respectively. Therefore, this method can successfully monitor longitudinal tear failures and ensure the safety of transportation.

A defect detection method for Akidzuki pears based on computer vision and deep learning

To quickly and accurately detect defects in Akidzuki (Pyrus pyrifolia Nakai) pears after harvest, this study aims to develop a method for Akidzuki pear defect detection based on computer vision and deep learning models. It mainly consists of obtaining high-quality images using an image acquisition system and proposing a new YOLO-AP detection model to identify defects in Akidzuki pears. The model uses YOLOv5 as the main architecture. The GhostDynamicConv (GDC) module is obtained by replacing the standard convolution in the Ghost module with a dynamic convolution. The C3-GhostDynamicConv (C3-GDC) module is obtained by replacing the Bottleneck module of C3 in Neck with the GDC module, simplifying the network while improving the model's accuracy. Meanwhile, Bottleneck Attention Module (BAM) is introduced after C3-GDC to refine the intermediate features. In addition, the original bounding box loss function is replaced with Wise-IoUv3 (WIoUv3) to accelerate the model convergence. The results demonstrate that YOLO-AP performs better in Akidzuki pear defect detection, with a mAP@0.5 of 0.939, a recall of 0.921, and a detection speed of 454.5 fps (2.2 ms per image). These values represent a 4.2 %, 3.5 %, and 1.4 % improvement over the baseline model. Comparing YOLO-AP with the updated YOLOv9 and other detection models, YOLO-AP is more accurate and faster. These findings demonstrated that the proposed method can detect Akidzuki pear defects in real time, efficiently and accurately, providing technical support for post-harvest defect detection.

Nutrient Stress Symptom Detection in Cucumber Seedlings Using Segmented Regression and a Mask Region-Based Convolutional Neural Network Model

The health monitoring of vegetable and fruit plants, especially during the critical seedling growth stage, is essential to protect them from various environmental stresses and prevent yield loss. Different environmental stresses may cause similar symptoms, making visual inspection alone unreliable and potentially leading to an incorrect diagnosis and delayed corrective actions. This study aimed to address these challenges by proposing a segmented regression model and a Mask R-CNN model for detecting the initiation time and symptoms of nutrient stress in cucumber seedlings within a controlled environment. Nutrient stress was induced by applying two different treatments: an indicative nutrient deficiency with an electrical conductivity (EC) of 0 dSm−1, and excess nutrients with a high-concentration nutrient solution and an EC of 6 dSm−1. Images of the seedlings were collected using an automatic image acquisition system two weeks after germination. The early initiation of nutrient stress was detected using a segmented regression analysis, which analyzed morphological and textural features extracted from the images. For the Mask R-CNN model, 800 seedling images were annotated based on the segmented regression analysis results. Nutrient-stressed seedlings were identified from the initiation day to 4.2 days after treatment application. The Mask R-CNN model, implemented using ResNet-101 for feature extraction, leveraged transfer learning to train the network with a smaller dataset, thereby reducing the processing time. This study identifies the top projected canopy area (TPCA), energy, entropy, and homogeneity as prospective indicators of nutritional deficits in cucumber seedlings. The results from the Mask R-CNN model are promising, with the best-fit image achieving an F1 score of 93.4%, a precision of 93%, and a recall of 94%. These findings demonstrate the effectiveness of the integrated statistical and machine learning (ML) methods for the early and accurate diagnosis of nutrient stress. The use of segmented regression for initial detection, followed by the Mask R-CNN for precise identification, emphasizes the potential of this approach to enhance agricultural practices. By facilitating the early detection and accurate diagnosis of nutrient stress, this approach allows for quicker and more precise treatments, which improve crop health and productivity. Future research could expand this methodology to other crop types and field conditions to enhance image processing techniques, and researchers may also integrate real-time monitoring systems.

Utilizing color analysis and image processing for the assessment of the color of block para rubber

This research explores the use of image processing and color analysis as a reliable approach for evaluating para rubber color quality. An automated color comparator prototype, based on the CIELAB color method and employing various image processing algorithms, is introduced. The research aims to enhance the precision of color analysis for block para rubbers (BPRs), a critical raw material in the rubber industry, impacted by environmental conditions during the sun-drying process. The study follows international standards for BPR specimen preparation and employs an image acquisition system, threshold techniques, and algorithms for color analysis. The proposed method aligns with Rubber Research Institute standards. The outcomes demonstrate the effectiveness of the automated system in accurately assessing the color values of BPRs, providing valuable insights for the rubber industry. This image processing technique offers advantages in natural rubber production for products requiring specific color characteristics.

Vision-Based Online Molten Pool Image Acquisition and Assessment for Quality Monitoring in Gas–Metal Arc Welding

In gas–metal arc welding (GMAW), the welding quality is influenced by various factors, including the welding conditions and external environment. Achieving a high welding quality requires careful observation and control. Welding monitoring is crucial for validating a planned welding process and assessing its quality. This study introduces a vision system for the online acquisition of clear molten pool images. It uses a small camera equipped with a complementary metal-oxide-semiconductor sensor during GMAW, as well as a method for constructing this system. The proposed vision system does not require triggering using devices such as field-programmable gate arrays, facilitating lower costs and online quality monitoring under various welding conditions during mild-steel GMAW. Accurate measurement of radiation from the weld is essential for clear observation of the molten pool during welding, emphasizing the importance of selecting an optical system with appropriate lenses and filters. In addition, appropriate digital camera parameters must be set for monitoring. High-quality images were successfully captured using the proposed method, and the image quality was evaluated using the blind/referenceless image spatial quality evaluator algorithm.

Computer-Based Experiment for the Motion of Spring Oscillator on a Linear Air Track Using Ultrasonic Sensor.

In the present paper, an affordable innovative physical experimental equipment consisting of an upper computer, an ultrasonic sensor module, and an Arduino microcontroller has been designed. The relationship between the position of the slider fixed on two springs and time is measured by using the ultrasonic sensor module. A system for slider motion data and image acquisition is constructed by using the LabVIEW interface of Arduino UNO R3. The purpose of this experiment is to demonstrate and interpret the propagation of waves represented by harmonic motion. The spring oscillator system including a slider and two springs is measured and recorded, and the motion can be realized using curve fitting to the wave equation in Sigmaplot. The vibration periods obtained from experimental measurements and curve fitting of the wave equation are 1.130 s and 1.165 s, respectively. The experimental data are in good agreement with the theoretical model. The experimental measurement results show that the maximum kinetic energy is 0.0792 J, the maximum potential energy is 0.0795 J, and the total energy at the position of half the amplitude is 0.0791 J. The results verify the mechanical energy conservation of spring oscillator system in a short time. This self-made instrument has improved the visualization and the automation level of the corresponding experiments.

A grader for fresh jujube with electromagnetic synchronous actuator assembly

AbstractIn recent years, the post‐harvest commercialization of fresh jujube sorting technology is backward, the efficiency of manual grading is low, and the application of machine vision technology in fruit grading has been a research hotspot. To improve the grading accuracy and speed of the fresh jujube machine vision grading machine, this study designs a method of fresh jujube machine vision grading using an electromagnetic synchronous actuator. The grading machine consists of an automatic distribution and arrangement system, an image acquisition and processing system, and a synchronous actuator assembly, which uses a sensitive electromagnet to adsorb a silicon steel switching buckling plate to execute the action. The image processing process includes an image color component, Sobel operator filtering, Otsu method binarization, and ellipse fitting to extract the color and size eigenvalues. The experimental results show that the date images can be acquired quickly and processed accurately with a processing speed of 50 ms per frame. In addition, the proposed actuator can be sensitively triggered with a response time of 40 ms. The grader successfully classified jujube into five size classes and one color class. The grading accuracy and non‐destructive rate reached 93.7% and 100%, respectively. The grading efficiency is 20 jujubes per second, with a maximum production rate of 1400 kilograms per hour. This grading machine realizes the requirements of high accuracy, high speed, and non‐destructive grading of fresh jujube and provides an important technical and theoretical basis for the development of machine vision jujube rapid grading technology.Practical applicationsIn recent years, sorting technology in the post‐harvest commercialization of fruits has been outdated, and manual grading has been inefficient. The application of machine vision technology in fruit grading has been a research hotspot in recent years. This study designs a machine vision grading of fresh dates with an electromagnetic synchronous actuator. The grader was composed of a roller conveyor, synchronous actuator assembly, image main processor, and actuator controlling co‐processor. Experimental results indicated that jujube images could be acquired rapidly and processed accurately, with a processing speed of 50 ms per frame. In addition, the proposed actuator could be sensitively triggered with a response time of 40 ms. Finally, the grader successfully sorted jujubes into five size classes and one color class. The grading accuracy rate and non‐destructive rate reached 93.7% and 100%, respectively. The grading efficiency was 20 jujubes per second, and the maximum productivity was 1400 kg/h.

A ridge-based detection algorithm with filament overlap identification for 2D mycelium network analysis

Many terrestrial ecosystems engage mycorrhizal symbiotic associations, potentially to enhance nutrition, increase resistance to soil-borne pests and diseases, and improve resilience and soil structure. Mycorrhizal fungi create dynamic networked structures through branching and anastomosis that connect multiple plants and consent to transport resources underground from nutrient-rich patches to demanding plants. Controlled laboratory experiments are fundamental to improving our knowledge of mycelium network growth dynamics and further understanding its role in preserving ecological niches. We propose a method for highly automated analysis of the mycelium network structure and other morphological properties, such as hyphal length, hyphal density, and number of crossing and branches, in 2D microscopy images of fungal samples. Available tools for automated network analyses suffer from overestimating network connectivity since filament crossings are not considered. In particular, we propose a) a ridge-based mycelium detection algorithm and b) a geometrical-based approach to identify overlapping filaments crossing each other. The algorithmic solution is evaluated on a total of 135 real mycelium sample images over different validation steps, originating from different datasets and having different characteristics, including background, contrast, image acquisition system, fungal species, and clearness (e.g., level of transparency, homogeneity, dirtiness of the medium) of the sample. Results show that 1) the proposed detection method can be used to measure the length of mycelium in an image, replacing manual tracing and allowing for less laborious analysis ρ̂c=0.96, 2) the filament detection is on par with state-of-the-art techniques F1=0.88−0.94 with a more intuitive parameterization, and 3) the proposed algorithm correctly identifies filament crossings F1=0.89 in most common cases, yielding a reduction in the overestimation of network connectivity. The latter feature consents to applying the proposed fully automated solution to complex and irregular fungal structures, advancing mycelium detection and reconstruction performance accuracy with respect to the state-of-the-art.

Precision detection method for ship shell plate molding based on neural radiance field

In this work, an integrated neural radiance field model ensemble measurement system was developed to measure the ship shell plate molding accuracy detection. This is the first work that uses the neural radiance field model to solve the issue of ship shell plate molding accuracy detection. The neural radiance field model of Instant-NGP was deployed to reconstruct the 3D model of the ship shell plate, which significantly reduced the model training time and ensured high reconstruction accuracy. An image acquisition system was constructed by combining the calculation results, and measurement experiments were carried out on three different sizes of ship shell plates in the production process. The accuracy, efficiency, and completeness of the proposed method were evaluated. The results show that the point cloud data reconstruction of the plate is accomplished in about 2.5 min, and the average error is less than 0.2 mm, which meets the requirements of the ship shell plate molding measurement and reflects high accuracy. Based on the experimental results, the optimal image parameters of the data set for the model to reconstruct the ship shell plate are given. Compared with the wooden formwork method, the active binocular vision method, and the reconstruction method based on MVSNet, our approach has the advantages of high precision, high efficiency, and low cost, which proves flexibility and robustness.

Circadian Rhythms in Tongue Features.

(1) Background: The aim of this study was to investigate the circadian rhythms of tongue features according to the effects of physiological phases over a 24 h period. (2) Methods: Fifteen healthy participants aged 20 to 69 years were recruited. The participants did not have current chronic diseases or past diseases and had to meet the inclusion and exclusion criteria. The participants stayed at the Gil Hospital for a duration of 2 nights and 3 days. On the first day, at 18:00, they consumed their allocated portions of food and water and then completed a questionnaire. At approximately 21:00, their tongue images were acquired using a computerized tongue image acquisition system, following which they slept for 8 h, commencing at 23:00. Measurements were taken from 07:00 through 21:00 on the second day, and the final acquisition was taken at 07:00 on the following morning, resulting in a total of eight images. The circadian rhythm was authenticated and quantified utilizing the single cosinor analysis, a technique for periodic regression analysis for fitting a 24 h cosine curve. (3) Results: Cosinor analysis revealed that all tongue features were significantly related to circadian rhythm. (4) Conclusions: The results of this study may be important for considering the time of day at which the tongue is observed and tongue status is evaluated.

Advancements in Visual Prosthetics: The Visionary Bionic Model

Abstract: The Visionary Bionic Model represents a paradigm shift in the field of visual prosthetics, offering new hope to individuals afflicted by retinal degenerative diseases. This journal paper provides a comprehensive overview of the foundational framework, operational mechanics, and advancements associated with the Visionary Bionic Model. Beginning with a historical context, the paper traces the evolution of bionic eyes from early conceptualizations to contemporary innovations. It explores the technological underpinnings of the Visionary Bionic Model, including microelectrode arrays, image acquisition systems, and signal processing algorithms. Clinical trials and case studies are examined to evaluate the safety and efficacy of the Visionary Bionic Model in restoring sight to individuals with visual impairments. The paper also discusses challenges and limitations inherent in current bionic vision systems, such ` resolution and compatibility issues, and proposes avenues for future research and development. Ethical considerations surrounding accessibility, affordability, and informed consent are addressed, emphasizing the importance of responsible innovation. Ultimately, the Visionary Bionic Model holds promise as a transformative solution for individuals living with retinal degenerative diseases, offering renewed possibilities for enhanced vision and quality of life.

Integrated photonic encoder for low power and high-speed image processing.

Modern lens designs are capable of resolving greater than 10 gigapixels, while advances in camera frame-rate and hyperspectral imaging have made data acquisition rates of Terapixel/second a real possibility. The main bottlenecks preventing such high data-rate systems are power consumption and data storage. In this work, we show that analog photonic encoders could address this challenge, enabling high-speed image compression using orders-of-magnitude lower power than digital electronics. Our approach relies on a silicon-photonics front-end to compress raw image data, foregoing energy-intensive image conditioning and reducing data storage requirements. The compression scheme uses a passive disordered photonic structure to perform kernel-type random projections of the raw image data with minimal power consumption and low latency. A back-end neural network can then reconstruct the original images with structural similarity exceeding 90%. This scheme has the potential to process data streams exceeding Terapixel/second using less than 100 fJ/pixel, providing a path to ultra-high-resolution data and image acquisition systems.

A Study on Predicting the Deviation of Jet Trajectory Falling Point under the Influence of Random Wind.

As one of the main external factors affecting the fire extinguishing accuracy of sprinkler systems, it is necessary to analyze and study random wind. However, in practical applications, there is little research on the impact of random wind on sprinkler fire extinguishing points. To address this issue, a new random wind acquisition system was constructed in this paper, and a method for predicting jet trajectory falling points in Random Forest (RF) under the influence of random wind was proposed, and compared with the commonly used prediction model Support Vector Machine (SVM). The method in this article reduces the error in the x direction of the 50 m prediction result from 2.11 m to 1.53 m, the error in the y direction from 0.64 m to 0.6 m, and the total mean absolute error (MAE) from 31.3 to 23.5. Simultaneously, predict the falling points of jet trajectory at different distances under the influence of random wind, to demonstrate the feasibility of the proposed method in practical applications. The experimental results show that the system and method proposed in this article can effectively improve the influence of random wind on the falling points of a jet trajectory. In summary, the image acquisition system and error prediction method proposed in this article have many potential applications in fire extinguishing.

Latency Evaluation in the Image Acquisition System Based on MTCA.4 Architecture for Plasma Diagnostics

ITER diagnostic systems provide measurements to the Plasma Control System (PCS) in real-time. These measurements are used for plasma control and machine protection. Latency is an important parameter in the assessment of such systems. It is a time gap between capturing an external event by hardware and finishing the processing of acquired data. PCS requires the diagnostic systems to introduce a maximum total latency of 10 to 100 ms, therefore, the systems need to be tested if they meet the requirements. The system evaluated in this paper is a reference real-time image acquisition system developed as a base for ITER diagnostic systems. It consists of hardware based on the Micro Telecommunications Computing Architecture (MicroTCA) standard, developed firmware, and software. It supports cameras with various interfaces. In the paper, two cameras, with a Camera Link and 1 GigE Vision interfaces were selected to perform latency evaluation. The paper presents two methods of measuring the latency of image acquisition. The first one is based on precise time stamping consecutive stages of acquisition. This approach allows for determining which step of acquisition takes more or less time. In consequence, the software or hardware can be optimized. The other one uses LED to evaluate a particular camera, by checking the time of camera reaction to the trigger. A dedicated testing framework is developed to perform automated tests to evaluate latency. It supports collecting and analyzing the results of measurements. Besides that, a dedicated hardware is used to perform the latency tests using LED. The results and discussion of the measurements are presented in the manuscript. They show the latency evaluated using earlier proposed methods, comparing the cameras used in the image acquisition system.

The use of intelligent real-time image acquisition system using data mining technology in news acquisition and editing practice

Aiming to address the timely dissemination of news information, this work explores the clever utilization of data mining (DM) technology and deep learning (DL) algorithms to construct an intelligent real-time news image acquisition system to meet the urgency of news dissemination needs. First, this work introduces an intelligent real-time news image acquisition system and provides a detailed analysis of its principles and advantages. Throughout this process, the crucial role of DM technology in news image classification and automation is emphasized, especially in dealing with rapidly evolving news events. Next, the work establishes an intelligent real-time news image acquisition model based on DL algorithms, which integrates the essence of DM technology. Through this fusion, the research objective is to enhance the performance of the news image acquisition system to achieve higher real-time and accuracy, which is vital for the swift delivery of news information. Finally, this work investigates the application of the intelligent news image acquisition system in network communication to ensure its adaptability to various network communication scenarios while maintaining accuracy and real-time capabilities. The research results demonstrate that the application of DM technology in combination with DL algorithms can effectively meet the practical needs of the news industry, enhancing the automation of news image processing and enabling faster information delivery to the audience. Notably, the AlexNet model employed performs exceptionally well, achieving recognition rates of up to 99.6% after data augmentation or equalization processing, with an accuracy of 90.9% and a high specificity of 93.38%. This indicates outstanding overall classification accuracy and negative class accuracy, even when distinguishing between news and non-news scenarios. These results clearly underscore the connection between DM technology and news acquisition and editing practices, and emphasize its potential to improve the efficiency and accuracy of real-time information dissemination. The research’s contribution and innovation lie in the fusion of DM technology with DL algorithms to build an intelligent real-time news image acquisition system. This fusion enhances the automation and classification performance of news images, thereby improving the real-time and accuracy of news information. Furthermore, the work strongly emphasizes improving the real-time and accuracy of the news image acquisition system to ensure the swift delivery of information, which is of utmost importance in rapidly changing news events.

Research on coal gangue size calibration based on perspective correction-parameter calibration synergy

The size calibration of coal gangue can help to determine the size range of coal gangue, which has a great influence on the grabbing strategy of coal gangue in the process of coal gangue sorting. However, at present, the size calibration of coal gangue has not been effectively studied. Based on this, this paper innovatively proposes a gangue size calibration method based on perspective correction-parameter calibration. Firstly, a coal gangue image acquisition and processing system was built to collect coal gangue images and preprocess the images. Secondly, the size calibration system model is established, including mathematical model and operation model. Finally, the influence of different heights and tilt angles on the accuracy and stability of the size calibration results were compared. The results show that when the camera height and the coal gangue height are 600 mm and the camera is perpendicular to the ground, the calibration accuracy and stability are the best, and the accuracy rate is more than 97 %. The research content of this paper provides theoretical support for the accurate positioning and precise sorting of coal gangue in the process of belt transportation.

General error analysis of matrix-operation-mode decomposition technique in few-mode fiber laser

The mode decomposition based on matrix operation (MDMO) is one of the fastest mode decomposition (MD) techniques, which is important to the few-mode fiber laser characterization and its applications. In this paper, the general error of the MDMO technique was analyzed, where different influencing factors, such as position deviation of the optical imaging system, coordinate deviation of the image acquisition system, aberrations, and mode distortion were considered. It is found that the MDMO technique based on far-field intensity distribution is less affected by optical imaging system position deviation, coordinate deviation of the image acquisition system, and mode distortion than those based on direct near-field decomposition. But far-field decomposition is more affected by aberration than those based on near-field decomposition. In particular, the numerical results show that the deviation of the coordinate axis direction is an important factor limiting the accuracy of MD. In addition, replacing the ideal eigenmode basis with a distorted eigenmode basis can effectively suppress the decrease in mode decomposition accuracy caused by fiber bending. Moreover, based on detailed numerical analysis results, fitting formulas for estimating the accuracy of the MDMO technique with imperfections are also provided, which provides a comprehensive method for evaluating the accuracy of the MDMO technique in practical engineering operations.