Image Acquisition Technology Research Articles

Image acquisition technology for unmanned aerial vehicles based on YOLO - Illustrated by the case of wind turbine blade inspection

Wind energy, as a renewable energy source, is becoming increasingly important. The maintenance and damage detection of wind turbine blades are particularly crucial. For this purpose, the study aims to optimize the You Only Look Once (YOLO) processing algorithm for drone images to improve the detection efficiency. Firstly, the damage images captured by drones are preprocessed and optimized, including deblurring, noise reduction, and image enhancement. Subsequently, the YOLOv5 model is improved in terms of structure and regression function, and a novel damage detection model is proposed. The research results indicated that the minimum loss function value of the improved model was 2.75, the average accuracy was 95 %, and the highest intersection over union was 91 %. After simulation testing, the detection effect of this model on abrasion, crackle, edge cracking, and coating peeling images was significantly better than other models in the same series. Its average time was as short as 2.43 s, reaching a maximum frame rate of 35.46. From this, the combination of drone image technology and improved image processing algorithm has a positive impact on improving the operational efficiency and safety of wind turbine blades. Compared with the traditional methods, the proposed model has significant advantages in terms of accuracy and real-time performance of damage detection, providing a new technical means for efficient maintenance of wind turbines. Meanwhile, the method shows high robustness and reliability in different types of damage detection, demonstrating the extensive potential in practical applications.

Pollen identification of three notorious illicit drug plants and its potential applications in forensic practice.

Opium poppy, coca and cannabis are raw materials for three notorious illicit drugs. For a long time, drug lords have been growing and smuggling these drugs in a variety of ways and channels and are continually finding new ways of trafficking their wares, which has led to the increasing difficulty of global drug enforcement. In the present paper, we propose an innovative pollen identification system for these important drug plants, which provides a tool for screening and detection of the drugs to aid in drug enforcement. By utilizing the characteristics of these fine particles, their abundant production, and high resistance to decay, we believe this tool could be applied in the following scenarios: detecting and dynamically monitoring drug cultivation activities; determining whether a suspect has been to fields of drug plants and determining whether the site has ever been planted with a drug plant and/or was involved in drug production. In the future, combined with microscope automatic image acquisition technology and intelligent image recognition technology, this pollen identification system is expected to be used to screen three notorious illicit drug plants, thus enhancing the efficiency of drug related crime investigations.

Immersive VR experience based on virtual robots in decorative material environment design simulation

Virtual robot and immersive VR experiences provide consumers with new ways of perception in the design process of decorative material environments. This experiential process is virtual, intuitive, and entertaining, and is widely recognized by consumers.The purpose of this study is to improve the processing effect of virtual images of decorative materials by image denoising algorithm. In this paper, the image acquisition and modeling technology based on sensor network and image denoising algorithm are used to process the acquired image data, and the processed image is applied to the virtual system of decorative materials. Through the application of image denoising algorithm, the noise in the image is reduced, and the clarity and authenticity of the virtual image of decorative materials are improved. This makes it possible to select the best decoration scheme more accurately when comprehensive calculation is carried out from various aspects such as aesthetic and functional data, and provides a new feasibility and method for environmental design and decoration material selection. The research results demonstrate that the system can achieve virtual image processing in environmental decoration scene design, thereby improving the level of indoor environmental art design.

An modified intelligent real-time crack detection method for bridge based on improved target detection algorithm and transfer learning

The combination of UAV camera and intelligent algorithm is a promising method for non-contact bridge crack detection. In this paper, an inspection tool based on UAV Image Acquisition Technology (UAVIAT) and Improved Intelligent Target Detection Technology (IITDT) called Improved Intelligent Real-Time Crack Detection Method for Bridges (IIRTCDMB) is proposed for efficient crack detection. The contributions of this paper are (1) The Squeeze-Excitement (SE) attention module is integrated into the target detection algorithm - You Only Look Once version 7 (YOLOv7) model to improve the learning ability of the feature channel. A Focal-efficient intersection over union (Focal-EIoU) loss function is also introduced to improve the regression accuracy of the model. As a result, a new crack image detection algorithm, YOLOv7-CD, is proposed. (2) A training process based on two-stage transfer learning (TSTL) is established, and hyper-parameter optimization of YOLOv7-CD is carried out. The feasibility and excellent performance of the proposed method are verified by applying it on the Cuntan Yangtze River Bridge. The results show that the average precision (AP) of the YOLOv7-CD model is improved by 3.19% compared with the original YOLOv7 model. After TSTL and hyperparameter optimization, the AP of the YOLOv7-CD model for bridge crack detection reaches 98.01%, which is higher than that of the popular target detection models. The IIRTCDMB proposed in this paper can acquire bridge surface images more safely and efficiently, and provide inspectors with more accurate structural crack information with lower computational and hardware requirements, which can provide technical support for the assessment of structural safety conditions and the formulation of maintenance programs.

Real-Time Image Acquisition and Stabilization Technology for Dynamic Robotic Dog Loading Video Acquisition Device

Real-Time Image Acquisition and Stabilization Technology for Dynamic Robotic Dog Loading Video Acquisition Device

SOFTWARE IMPLEMENTATION OF ALGORITHMS FOR SELECTING THE OUTLINES OF OBJECTS IMAGES IN INTELLECTUAL VIDEO SURVEILLANCE SYSTEMS

The rapid advancement in digital image acquisition and transmission technologies poses challenges in processing vast volumes of video information streams. Images serve as crucial sources of decision-making information across a wide range of tasks. Key objectives addressed by intelligent video surveillance systems include object identification, trajectory determination, object speed measurement, and real-time detection of critical events for securing premises. Among the primary operations in intelligent video surveillance systems is the extraction of object contours from images, as contours contain essential information for object recognition based on their shape. This approach excludes internal image points, significantly reducing the amount of processed information and enabling real-time image analysis. Contour analysis encompasses methods for selecting, describing, and processing image contours to describe, store, compare, and search for objects based on their external contours. This methodology effectively addresses fundamental challenges in pattern recognition, such as transferring, rotating, and scaling object images. The paper discusses prominent contour analysis methods and presents software implementations of algorithms for extracting object outlines in intelligent video surveillance systems. A promising future direction involves synthesizing algorithms that extract object contours using a two-scale statistical image model.

A Review of Effect of Mineral Admixtures on Appearance Quality of Fair-Faced Concrete and Techniques for Their Measurement

The appearance of fair-faced concrete is crucial, and it can be enhanced by incorporating an appropriate amount of mineral admixture. Different mineral admixtures have varying effects on the appearance quality of fair-faced concrete. For instance, the addition of fly ash helps control color differences and bubble formation on the concrete surface, while slag powder effectively controls its color and finish. In this review, the impact of using various mineral admixtures, such as silica fume, rice husk ash, limestone powder, and seashell powder, in fair-faced concrete on its appearance quality is examined. The effective combination of mineral admixtures made from industrial by-products or solid waste with fair-faced concrete can pave the way for new directions in the green and sustainable development of construction materials. This review also discusses the difficulties in objectively measuring the appearance quality of concrete and the various methods for the acquisition and evaluation of appearance images. New techniques for acquiring and evaluating information about concrete surfaces have been developed with advancements in image acquisition and processing technology. These techniques complement traditional manual inspection methods. The 3D Alicona system is advantageous for identifying air bubbles on concrete surfaces, the atomic-force microscope detects surface roughness, and the Orbital large-format scanner is ideal for use in large-scale engineering applications. Also, evaluation methods for different image processing software are presented in this article. This information offers a useful reference for future research and practical application.

Real-time self-supervised tone curve estimation for HDR image

High dynamic range (HDR) image acquisition technology can record real-scene information. However, most display devices only support standard dynamic range images; therefore, dynamic range compression via tone mapping operators is a key technology for HDR image visualization. Recently, deep learning has achieved significantly better results than traditional methods in tone mapping; however, some problems remain. On the one hand, the labels of datasets are usually selected from the results of existing traditional methods, which limit the quality of newly generated results. On the other hand, these algorithms require huge computational resources and cannot be practically applied. As the dynamic range can be compressed using an intuitive and simple sigmoid curve mapping, a lightweight network is designed in this study to estimate the patch-wise tone curve parameters for HDR images. Simultaneously, the differentiable approximation of tone mapped image quality assessment is introduced as a self-supervised loss term so that the method only needs HDR data as training data. Experimental results demonstrate that the proposed method achieves better results than existing methods in both objective and subjective metrics at a low computational cost.

A Three-Dimensional Technique for The Visualization of Mitochondrial Ultrastructural Changes in Pancreatic Cancer Cells

Understanding the dynamic features of the cell organelle ultrastructure, which is not only rich in unknown information but also sophisticated from a three-dimensional (3D) perspective, is critical for mechanistic studies. Electron microscopy (EM) offers good imaging depth and allows for the reconstruction of high-resolution image stacks to investigate the ultrastructural morphology of cellular organelles even at the nanometer scale; therefore, 3D reconstruction is gaining importance due to its incomparable advantages. Scanning electron microscopy (SEM) provides a high-throughput image acquisition technology that allows for reconstructing large structures in 3D from the same region of interest in consecutive slices. Therefore, the application of SEM in large-scale 3D reconstruction to restore the true 3D ultrastructure of organelles is becoming increasingly common. In this protocol, we suggest a combination of serial ultrathin section and 3D reconstruction techniques to study mitochondrial cristae in pancreatic cancer cells. The details of how these techniques are performed are described in this protocol in a step-by-step manner, including the osmium-thiocarbohydrazide-osmium (OTO) method, the serial ultrathin section imaging, and the visualization display.

Developing Multi-Label Breast Cancer Screening using Efficientnetb3 Feature Extractor

Breast cancer is the first new case occurring and the fifth death-causing cancer disease in 2020, which collapses the families as women are the pillar of the family members. 3D image acquisition technology increases treatment methods and decreases the rate of breast cancer death. Furthermore, single-label breast cancer classifications are deployed to solve the problem. However, it cannot convey more information about mammogram images. In the area where the shortage of datasets is there as in the medical image, data augmentation and transfer learning are the best solution. A Multi-Label Breast Cancer Screening is a deep learning model using a transfer learning model that gives information on finding, density, and pathology of patients' breast mammograms to the radiologists, aiming for life-saving and supporting the family. The existing model shows low accuracy on feature extraction and more layers with a very large number of nodes in a layer in the classification part. In this study, the best feature extractor for this particular work is investigated and identified as efficientnetb3, and ONN is used for the classification part. The proposed model outperforms the previous work in all evaluation metrics with a 13.25% f1_score, 53% hamming loss, 36.7% coverage error, and 12.5 an exact match. And , the number of parameters were decreased from 134M to 20M which was resulted from the optimizing of classification part of the model. The developed model with ONN in the classification part has made the best improvement over the existing model in terms of evaluation metrics and network performances.

UAV Platforms and the SfM-MVS Approach in the 3D Surveys and Modelling: A Review in the Cultural Heritage Field

In recent years, structure from motion (SfM) and multi-view stereo (MVS) algorithms have been successfully applied to stereo images generated by cameras mounted on unmanned aerial vehicle (UAV) platforms to build 3D models. Indeed, the approach based on the combination of SfM-MVS and UAV-generated images allows for cost-effective acquisition, fast and automated processing, and detailed and accurate reconstruction of 3D models. As a consequence, this approach has become very popular for representation, management, and conservation in the field of cultural heritage (CH). Therefore, this review paper discusses the use of UAV photogrammetry in CH environments with a focus on state of the art trends and best practices in image acquisition technologies and 3D model-building software. In particular, this paper intends to emphasise the different techniques of image acquisition and processing in relation to the different platforms and navigation systems available, as well as to analyse and deepen the aspects of 3D reconstruction that efficiently describe the entire photogrammetric process, providing further insights for new applications in different fields, such as structural engineering and conservation and maintenance restoration of sites and structures belonging to the CH field.

Automated seed identification with computer vision: challenges and opportunities

Applying advanced technologies such as computer vision is highly desirable in seed testing. Among testing needs, computer vision is a feasible technology for conducting seed and seedling classification used in purity analysis and in germination tests. This review focuses on seed identification that currently encounters extreme challenges due to a shortage of expertise, time-consuming training and operation, and the need for large numbers of reference specimens. The reviewed computer vision techniques and application strategies also apply to other methods in seed testing. The review describes the development of machine learning-based computer vision in automating seed identification and their limitations in feature extraction and accuracy. As a subset of machine learning techniques, deep learning has been applied successfully in many agricultural domains, which presents potential opportunities for its application in seed identification and seed testing. To facilitate application in seed testing, the challenges of deep learning-based computer vision systems are summarised through analysing their application in other agricultural domains. It is recommended to accelerate the application in seed testing by optimising procedures or approaches in image acquisition technologies, dataset construction and model development. A concept flow chart for using computer vision systems is proposed to advance computer-assisted seed identification.

Extraction and Analysis of Foot Bone Shape Features Based on Deep Learning.

With the rapid development of artificial intelligence, more and more researchers and research institutions begin to pay attention to the bone feature recognition field. Human bone movement is very complex, and human bone shape recognition technology can be widely used in medical treatment, sports, and other fields. At present, there are mainly two kinds of methods for extracting the shape features of human foot bone based on optical image acquisition technology and sensor information perception technology. However, due to the interference factors such as target posture change, camera shake, and individual behavior differences, it is still a very challenging task to design a robust algorithm for extraction and analysis of foot bone shape features. In recent years, convolutional neural network- (CNN-) based foot contour feature recognition methods emerge one after another and have made breakthrough progress. How to use and how to fully explore the potential relationship of various characteristics contained in the foot bone data and how to enhance the robustness of view changes and other aspects need to be further studied. In this context, this paper proposed an improved CNN model, which not only has the capability of deep feature extraction of the CNN model but also can obtain the optimal model parameters with the combination of particle swarm optimization algorithm. The effectiveness of the proposed method in the extraction and analysis of foot bone shape features is verified in the simulation experiment.

Statistical quality control using image intelligence: A sparse learning approach

AbstractAdvances in image acquisition technology have made it convenient and economic to collect large amounts of image data. In manufacturing and service industries, images are increasingly used for quality control purposes because of their ability to quickly provide information about product geometry, surface defects, and nonconforming patterns. In production line monitoring, image data often take the form of image streams in the sense that images from the process are being collected over time. In such applications, a fundamental task is to properly analyze image data streams. This image monitoring problem is challenging for several reasons. First, images often have complicated structures such as edges and singularities, which render many traditional smoothing methods inapplicable. Second, a typical grayscale image contains tens of thousands of pixels, so the data is high‐dimensional. It has been shown in the statistical process control (SPC) literature that conventional multivariate control charts have limited power of detecting process shifts when the data dimension is high. In this article, we propose to transform images using a two‐dimensional wavelet basis and monitor the wavelet coefficients by sparse learning‐based multivariate control charts. By adapting the sparse learning algorithm to our quality control problem, the proposed method is able to detect shifts in the wavelet coefficients in a timely fashion and simultaneously identify those shifted coefficients. Combining this feature with the localization property of the wavelet basis, our method also enables accurate diagnosis of faulty image regions. In addition, the proposed charting statistics have explicit formulas, so they are easy to compute. Theoretical justifications and numerical comparisons with an existing method show that our method works well in applications.

Study on the Influence of Initial Water Content on One-Dimensional Freezing Process of the Qinghai-Tibet Silty Clay by Using Digital Image Technology

The water content of the foundation soil will change dynamically as a result of rainfall, snowfall, and ground surface evaporation, leading to a significant change in frost heave properties of the foundation soil in cold regions. One-dimensional freezing tests of the Qinghai-Tibet silty clay with three different initial water contents in an open system were carried out using CCD image acquisition technology and computed tomography (CT) scanning technology in combination with the traditional soil freeze-thaw test system. The heat conduct process, cryostructure formation, frost heave development, unfrozen zone consolidation, ice segregation, water migration, and redistribution during soil freezing are studied comprehensively. The results show that increasing the sample’s initial water content will lead to the increase of frozen depth, ladder-like cryostructures in vertical sections, more structural polygons in horizontal sections, and also more obvious ice lens segregation, unfrozen zone consolidation, and water migration during the freezing of soil samples.

Moisture migration in the Qinghai-Tibet silty clay within an added quartz sand layer under one-dimensional freezing

This paper investigated the freezing processes of Qinghai-Tibet silty clay within an added quartz sand layer (QSL) under different one-dimensional freezing temperature gradients by using computed tomography scanning and image acquisition technologies in an open system. The heat transfer, cryostructure formation and water migration processes and mechanisms were discussed. The results reveal that the temperature of the samples was essentially steady within 12 h, and the addition of the QSL has a significant impact on the temperature distribution in the samples. The moisture content of the QSL and the frozen part of the sample increased, while the moisture content of the unfrozen zone decreased significantly after being frozen for 120 h. The QSL was able to effectively limit liquid water migration, however, there was still vapor migration in the samples. Ladder-like and net-like cryostructures formed in the vertical sections of the sample while polygon cracks formed in the horizontal sections, which providing pathways for moisture migration. The saturated vapor pressure difference which calculated by the Antony Equation can be used as the driving force for vapor migration from the unfrozen zone to the frozen zone. From both an experimental and theoretical standpoint, this work is intended to aid research into the physical process and mechanism of sand interbedded stratum and foundation freezing.

Customized root-analogue dental implants - Procedure and errors associated with image acquisition, treatment, and manufacturing technology in an experimental study on a cadaver dog mandible

The global market for dental implants is constantly evolving. To overcome the limitations of conventional strategies – invasive surgeries and bone loss around the implant-, new approaches, namely the development of CAD/CAM customized implants, are emerging as promising solutions for replacing missing teeth, thus avoiding destruction of bone by drilling it. Despite the efforts that are being carried out, the designing, including image acquisition, and manufacturing efficacy of such rehabilitation therapy is not easily found in the literature. In this sense, this research work aimed to assess the errors associated with the image acquisition, treatment and manufacturing of Customized Root-Analogue Implants (CRAI). The experimental procedure, carried out in a dog cadaveric mandible, encompassed the image acquisition and treatment and the subsequent manufacturing of four different CRAI. Two Image acquisition methods (CBCT and X-ray), and different software for their treatment were used, and the final CRAI were manufactured by Computer Numerical Control. Results revealed the CBCT scan allowed to properly visualize the original teeth contours. However, all the obtained teeth geometries presented smaller surface areas when compared to the original teeth indicating however a systematic predictable error. On the other hand, when comparing the surface area of the digital and the manufactured teeth, very small differences were observed, which indicates that the CNC manufacturing technique is a promising solution for such applications. When placing the produced CRAI in the dog mandible it was possible to observe a lack of congruency between the CRAI and bone, mainly due to image acquisition process errors and the destruction of the periodontal ligament during the soft tissues removal process. Thus, CBCT images seem to be a feasible route to design root-analogue implants, and, if needed, a compensation strategy for the systematic image acquisition error may be implemented.

Recent advances in methods for in situ root phenotyping.

Roots assist plants in absorbing water and nutrients from soil. Thus, they are vital to the survival of nearly all land plants, considering that plants cannot move to seek optimal environmental conditions. Crop species with optimal root system are essential for future food security and key to improving agricultural productivity and sustainability. Root systems can be improved and bred to acquire soil resources efficiently and effectively. This can also reduce adverse environmental impacts by decreasing the need for fertilization and fresh water. Therefore, there is a need to improve and breed crop cultivars with favorable root system. However, the lack of high-throughput root phenotyping tools for characterizing root traits in situ is a barrier to breeding for root system improvement. In recent years, many breakthroughs in the measurement and analysis of roots in a root system have been made. Here, we describe the major advances in root image acquisition and analysis technologies and summarize the advantages and disadvantages of each method. Furthermore, we look forward to the future development direction and trend of root phenotyping methods. This review aims to aid researchers in choosing a more appropriate method for improving the root system.

Informatization Teaching Mode of Vision Sensor Digital Image and Fusion Association Rule Mining Algorithm

Informatization teaching mode emerged under the tide of informatization development. The way of information dissemination and the process of human cognition have undergone tremendous changes in the era of informatization. Based on the vision sensor digital image and the fusion association rule mining algorithm, this paper studies the informatization teaching mode, aiming to analyze the influence of the vision sensor digital image and the fusion association rule mining algorithm on the foreign language teaching and enhance development decisions. The structure of digital image is analyzed by using image acquisition and processing technology and data transmission, and the digital image correlation method is explored. The use of data mining algorithms to conduct statistics on data integration related teaching documents in the past five years provides a strong basis for the data of the research process. In addition, in order to conduct a more detailed and comprehensive research on the informatization teaching model, the research scope was narrowed and the survey method was adopted to understand and collect data from relevant personnel and finally put forward constructive opinions for the purpose of the existing problems. The findings of the study demonstrated that the application of digital images can greatly expand the teaching content and greatly improve the creativity, experience, inclusiveness, and interactivity of teaching applications. The most obvious one is the sense of experience. The measured index has increased by 5.4 points.

Mapping the human connectome using diffusion MRI at 300 mT/m gradient strength: Methodological advances and scientific impact

Tremendous efforts have been made in the last decade to advance cutting-edge MRI technology in pursuit of mapping structural connectivity in the living human brain with unprecedented sensitivity and speed. The first Connectom 3T MRI scanner equipped with a 300 mT/m whole-body gradient system was installed at the Massachusetts General Hospital in 2011 and was specifically constructed as part of the Human Connectome Project. Since that time, numerous technological advances have been made to enable the broader use of the Connectom high gradient system for diffusion tractography and tissue microstructure studies and leverage its unique advantages and sensitivity to resolving macroscopic and microscopic structural information in neural tissue for clinical and neuroscientific studies. The goal of this review article is to summarize the technical developments that have emerged in the last decade to support and promote large-scale and scientific studies of the human brain using the Connectom scanner. We provide a brief historical perspective on the development of Connectom gradient technology and the efforts that led to the installation of three other Connectom 3T MRI scanners worldwide – one in the United Kingdom in Cardiff, Wales, another in continental Europe in Leipzig, Germany, and the latest in Asia in Shanghai, China. We summarize the key developments in gradient hardware and image acquisition technology that have formed the backbone of Connectom-related research efforts, including the rich array of high-sensitivity receiver coils, pulse sequences, image artifact correction strategies and data preprocessing methods needed to optimize the quality of high-gradient strength diffusion MRI data for subsequent analyses. Finally, we review the scientific impact of the Connectom MRI scanner, including advances in diffusion tractography, tissue microstructural imaging, ex vivo validation, and clinical investigations that have been enabled by Connectom technology. We conclude with brief insights into the unique value of strong gradients for diffusion MRI and where the field is headed in the coming years.