Application Of Computer Vision Technology Research Articles

A Multi-View Real-Time Approach for Rapid Point Cloud Acquisition and Reconstruction in Goats

The body size, shape, weight, and scoring of goats are crucial indicators for assessing their growth, health, and meat production. The application of computer vision technology to measure these parameters is becoming increasingly prevalent. However, in real farm environments, obstacles, such as fences, ground conditions, and dust, pose significant challenges for obtaining accurate goat point cloud data. These obstacles lead to difficulties in rapid data extraction and result in incomplete reconstructions, causing substantial measurement errors. To address these challenges, we developed a system for real-time, non-contact acquisition, extraction, and reconstruction of goat point clouds using three depth cameras. The system operates in a scenario where goats walk naturally through a designated channel, and bidirectional distributed triggering logic is employed to ensure real-time acquisition of the point cloud. We also designed a noise recognition and filtering method tailored to handle complex environmental interferences found on farms, enabling automatic extraction of the goat point cloud. Furthermore, a distributed point cloud completion algorithm was developed to reconstruct missing sections of the goat point cloud caused by unavoidable factors such as railings and dust. Measurements of body height, body slant length, and chest circumference were calculated separately with deviation of no more than 25 mm and an average error of 3.1%. The system processes each goat in an average time of 3–5 s. This method provides rapid and accurate extraction and complementary reconstruction of 3D point clouds of goats in motion on real farms, without human intervention. It offers a valuable technological solution for non-contact monitoring and evaluation of goat body size, weight, shape, and appearance.

Kidney medicine meets computer vision: a bibliometric analysis.

Rapid advances in computer vision (CV) have the potential to facilitate the examination, diagnosis, and treatment of diseases of the kidney. The bibliometric study aims to explore the research landscape and evolving research focus of the application of CV in kidney medicine research. The Web of Science Core Collection was utilized to identify publications related to the research or applications of CV technology in the field of kidney medicine from January 1, 1900, to December 31, 2022. We analyzed emerging research trends, highly influential publications and journals, prolific researchers, countries/regions, research institutions, co-authorship networks, and co-occurrence networks. Bibliographic information was analyzed and visualized using Python, Matplotlib, Seaborn, HistCite, and Vosviewer. There was an increasing trend in the number of publications on CV-based kidney medicine research. These publications mainly focused on medical image processing, surgical procedures, medical image analysis/diagnosis, as well as the application and innovation of CV technology in medical imaging. The United States is currently the leading country in terms of the quantities of published articles and international collaborations, followed by China. Deep learning-based segmentation and machine learning-based texture analysis are the most commonly used techniques in this field. Regarding research hotspot trends, CV algorithms are shifting toward artificial intelligence, and research objects are expanding to encompass a wider range of kidney-related objects, with data dimensions used in research transitioning from 2D to 3D while simultaneously incorporating more diverse data modalities. The present study provides a scientometric overview of the current progress in the research and application of CV technology in kidney medicine research. Through the use of bibliometric analysis and network visualization, we elucidate emerging trends, key sources, leading institutions, and popular topics. Our findings and analysis are expected to provide valuable insights for future research on the use of CV in kidney medicine research.

Application of Computer vision Technologies in Health-Improving Physical Education

Introduction. The article presents the areas of use of computer vision technologies in the field of physical education and sports activities. Methodology. Innovative methods for assessing motor action based on video material, pattern recognition, object tracking are analyzed, the possibility of automated control of motor action based on the use of artificial intelligence and neural networks is shown. Particular attention is paid to the possibilities to integrate computer vision technologies from the field of professional sports and fitness industry into fitness physical culture, educational process. Results. A computer program based on the MediaPipe system has been developed for automated assessment of the effectiveness of physical exercise based on biomechanical properties of motor action, taking into account individual proportions of the body, determining the load on key points of the body in order to obtain an optimal health effect. Conclusions. The prospects for using this program to optimize the methodology of health-improving physical education are discussed.

Computer vision solution for uniform adherence in gastronomy schools: An artificial intelligence case study

This research study presents an innovative application of computer vision technology in culinary education to ensure consistent student uniform adherence, crucial to accomplishing hygiene, safety, and professionalism standards. The proposed approach utilizes the Cross-Industry Standard Process for Data Mining (CRISP-DM) methodology to generate a computer vision application prototype to identify specific culinary uniforms components, such as chef's jackets, aprons, hats, and pants. The development process using LandingLens, a code-free platform, involves several stages: business and data understanding, preparation, modeling, evaluation, and deployment. The final model was training with 77 images, and the application deployment was tested using 38 images. Results demonstrate the potential of artificial intelligence to enhance operational efficiency and uphold professional standards in culinary education. Integrating computer vision addresses the challenges associated with manual monitoring and opens opportunities for broader adoption of technology in culinary pedagogy and training.

Application of computer vision technology in surface damage detection and analysis of shedthin tiles in China: a case study of the classical gardens of Suzhou

In computer artificial intelligence, there is great potential in research on the protection of Suzhou's traditional gardens, a world cultural heritage site. As a special material in Suzhou's traditional garden architecture, shedthin tile is widely used in roof base laying and is one of the important materials for building roofs. However, professionals need to reach the roof and spend much time and effort assessing the damage before repairing it. Therefore, the main goals of this study are to investigate a machine learning-based method for finding targets and determining the type of surface damage on a shedthin tile using the YOLOv4 model trained in this study. Using 500 shedthin tile on-site photos as training samples, the model was trained for 750 epochs. The main results of this study are as follows: (1) An object detection method based on machine learning can efficiently and accurately identify damage content, overcoming the manpower and time–cost limitations of traditional assessment methods. (2) The detection model in this study has an accuracy of 85.89% for water stain recognition of shedthin tiles, 93.29% for surface scaling, 87.37% for color aberration, and 96.15% for too wide a gap. The comprehensive accuracy is 90.20%, which meets the basic testing requirements. (3) The model demonstrated its robustness and reliability in complex environments in application tests in actual scenarios, providing a methodological reference for computer vision and target detection technology in cultural heritage protection.

SRT: Swin-residual transformer for benign and malignant nodules classification in thyroid ultrasound images

With the advancement of deep learning technology, computer-aided diagnosis (CAD) is playing an increasing role in the field of medical diagnosis. In particular, the emergence of Transformer-based models has led to a wider application of computer vision technology in the field of medical image processing. In the diagnosis of thyroid diseases, the diagnosis of benign and malignant thyroid nodules based on the TI-RADS classification is greatly influenced by the subjective judgment of ultrasonographers, and at the same time, it also brings an extremely heavy workload to ultrasonographers. To address this, we propose Swin-Residual Transformer (SRT) in this paper, which incorporates residual blocks and triplet loss into Swin Transformer (SwinT). It improves the sensitivity to global and localized features of thyroid nodules and better distinguishes small feature differences. In our exploratory experiments, SRT model achieves an accuracy of 0.8832 with an AUC of 0.8660, outperforming state-of-the-art convolutional neural network (CNN) and Transformer models. Also, ablation experiments have demonstrated the improved performance in the thyroid nodule classification task after introducing residual blocks and triple loss. These results validate the potential of the proposed SRT model to improve the diagnosis of thyroid nodules' ultrasound images. It also provides a feasible guarantee to avoid excessive puncture sampling of thyroid nodules in future clinical diagnosis.

WheatLFANet: in-field detection and counting of wheat heads with high-real-time global regression network

BackgroundDetection and counting of wheat heads are of crucial importance in the field of plant science, as they can be used for crop field management, yield prediction, and phenotype analysis. With the widespread application of computer vision technology in plant science, monitoring of automated high-throughput plant phenotyping platforms has become possible. Currently, many innovative methods and new technologies have been proposed that have made significant progress in the accuracy and robustness of wheat head recognition. Nevertheless, these methods are often built on high-performance computing devices and lack practicality. In resource-limited situations, these methods may not be effectively applied and deployed, thereby failing to meet the needs of practical applications.ResultsIn our recent research on maize tassels, we proposed TasselLFANet, the most advanced neural network for detecting and counting maize tassels. Building on this work, we have now developed a high-real-time lightweight neural network called WheatLFANet for wheat head detection. WheatLFANet features a more compact encoder-decoder structure and an effective multi-dimensional information mapping fusion strategy, allowing it to run efficiently on low-end devices while maintaining high accuracy and practicality. According to the evaluation report on the global wheat head detection dataset, WheatLFANet outperforms other state-of-the-art methods with an average precision AP of 0.900 and an R2 value of 0.949 between predicted values and ground truth values. Moreover, it runs significantly faster than all other methods by an order of magnitude (TasselLFANet: FPS: 61).ConclusionsExtensive experiments have shown that WheatLFANet exhibits better generalization ability than other state-of-the-art methods, and achieved a speed increase of an order of magnitude while maintaining accuracy. The success of this study demonstrates the feasibility of achieving real-time, lightweight detection of wheat heads on low-end devices, and also indicates the usefulness of simple yet powerful neural network designs.

Fuzzy Comprehensive Evaluation Model of Project Investment Risk Based on Computer Vision Technology

With the development of the economy and real-time embedded systems and the progress of science and technology, people’s economic income forms have undergone tremendous changes, and the concept of financial management has become clearer in people’s property income arrangements. Project investment is one of the most popular financial management methods in the era of big data. Both large enterprise groups and individual petty bourgeoisie groups have begun to pay attention to the risks and benefits brought by the new financial management method of project investment. This paper’s goal is to develop a fuzzy comprehensive evaluation (FCE) model for project investment risk based on computer vision technology and explore the application of computer vision technology in project investment risk evaluation. This article first uses a real-time embedded system to understand the basic process of project investment and select 10 investment experts for risk assessment, risks, and causes of the risks through literature research and case analysis. Then, this paper establishes a model of fuzzy comprehensive evaluation of project investment risk through computer vision technology, real-time embedded systems, and neural network models in big data and artificial intelligence technology to realize the analysis and prediction of project investment risk. The fuzzy comprehensive evaluation method and analytic hierarchy process (AHP) are used in this evaluation model to evaluate and forecast project investment risks. In addition, this paper also trains and tests the risk evaluation model of this research through the support vector machine classification algorithm, the real-time embedded system, and the average random consistency index. The research shows that the fuzzy comprehensive evaluation model of this study has higher accuracy for project investment risk evaluation than other risk evaluation methods. For example, for the investment risk of chemical fiber projects, this research model evaluated the factors such as organization, management, technology, and economy and found that the risks were all higher than 21.36%, which concluded that the overall investment risk of chemical fiber projects was relatively high.

Computer Vision Technology for Monitoring of Indoor and Outdoor Environments and HVAC Equipment: A Review.

Artificial intelligence technologies such as computer vision (CV), machine learning, Internet of Things (IoT), and robotics have advanced rapidly in recent years. The new technologies provide non-contact measurements in three areas: indoor environmental monitoring, outdoor environ-mental monitoring, and equipment monitoring. This paper summarizes the specific applications of non-contact measurement based on infrared images and visible images in the areas of personnel skin temperature, position posture, the urban physical environment, building construction safety, and equipment operation status. At the same time, the challenges and opportunities associated with the application of CV technology are anticipated.

A Crack Segmentation Model Combining Morphological Network and Multiple Loss Mechanism.

With the wide application of computer vision technology and deep-learning theory in engineering, the image-based detection of cracks in structures such as pipelines, pavements and dams has received more and more attention. Aiming at the problems of high cost, low efficiency and poor detection accuracy in traditional crack detection methods, this paper proposes a crack segmentation network by combining a morphological network and a multiple-loss mechanism. First, for improving the identification of cracks with different resolutions, the U-Net network is used to extract multi-scale features from the crack image. Second, for eliminating the effect of polarized light on the cracks under different illuminations, the extracted crack features are further morphologically processed by a white-top hat transform and a black-bottom hat transform. Finally, a multi-loss mechanism is designed to solve the problem of the inaccurate segmentation of cracks on a single scale. Extensive experiments are carried out on five open crack datasets: Crack500, CrackTree200, CFD, AEL and GAPs384. The experimental results showed that the average ODS, OIS, AIU, sODS and sOIS are 75.7%, 73.9%, 36.4%, 52.4% and 52.2%, respectively. Compared with state-of-the-art methods, the proposed method achieves better crack segmentation performance. Ablation experiments also verified the effectiveness of each module in the algorithm.

Oral CT Image Processing Based on Oral CT Image Filtering Algorithm.

The application of computer vision technology in the medical field provides more accurate technical support for oral disease detection. The research proposes the CT image denoising algorithm based on wavelet and bilateral filtering. Through the study of the imaging principle of CT image and the CT image acquisition scene, the CT image data is filtered by wavelet and bilateral filtering algorithm, and the algorithm is proposed from the peak signal-to-noise ratio, structural similarity, and the effective detection of three-dimensional image construction of the image. The test results show that the proposed algorithm has excellent performance in the aspects of peak signal-to-noise ratio, structural similarity, and error of mean square. When the proportions of Gaussian noise are 10%, 20%, 30%, 40%, and 50%, the MSE error values of the proposed algorithm are 0.002, 0.004, 0.006, and 0.007, respectively, and the performance is the best in the comparison of multiple algorithms. The contents of the research provide an important theoretical reference for the treatment of clinical oral diseases.

Identification of vehicle axle loads based on visual measurement

Monitoring vehicle axle loads is very important for preventing infrastructure degradation and traffic accidents. However, developing an accurate, cost-effective, and easy-to-implement axle load identification technology still remains challenging. In this study, a vehicle axle load identification method based on visual measurement is proposed. The principle of the method is that each axle load of the vehicle is determined based on the vehicle unsprung masses, the ratio of vehicular sprung mass to unsprung mass, and the centroid position of the sprung mass. While the vehicle unsprung masses can be treated as a known with the vehicle model identified, the mass ratio can be predicted from the visually captured free damped vibrations of the vehicle as it passes over a speed bump. To be more specific, the vibration responses of a vehicle passing over a speed bump are firstly measured by a camera, and the vehicle system matrix, which is composed of the mass matrix, stiffness matrix, and damping matrix, can be obtained from the vehicle responses. The axle load can then be determined based on the element ratios in the system matrix of the vehicle whose unsprung masses are known. The performance of the proposed method was evaluated by numerical simulations and field tests. The results show that the errors of the simulation results are all less than 1% under ideal conditions. In the field test, the average error in identifying axle loads at different vehicle speeds was 7.01% when the vehicle speed was below 20 m s−1, which proved the accuracy and effectiveness of the proposed method. This study has demonstrated a novel application of computer vision technology to identify the axle loads of moving vehicles. The proposed method does not require installation of sensors on the roadway or the vehicle, making it a promising alternative for traditional weigh-in-motion systems.

Automatic shrimp counting method using local images and lightweight YOLOv4

Shrimp counting is a fundamental operation for biomass estimation in shrimp culture. It is also vital for achieving reasonable feeding and improving breeding efficiency. The application of computer vision technology in the counting of aquatic products is nondestructive and highly efficient. However, owing to the small size, transparent body, and complex background of shrimp in the actual culture environment, existing methods cannot ensure the lightweight of the model applied while satisfying the counting accuracy, and it is difficult to achieve accurate real-time shrimp counting. Therefore, an automatic shrimp counting method using local images and lightweight YOLOv4 (Light-YOLOv4) was proposed in this study. Multiple local shrimp images were randomly cropped from the original top-view images previously collected using image processing technologies to construct a counting dataset. Subsequently, a local shrimp counting model based on Light-YOLOv4 is constructed and trained using transfer learning. Based on the trained model, the number of shrimp in each local shrimp image was predicted. The number of shrimp in the original shrimp image was obtained through a merging process, and the number of shrimp in the culture area was determined using the frame average method. The method was tested on a real shrimp dataset, and the Light-YOLOv4 local shrimp counting model achieved a counting precision of 92.12%, recall of 94.21%, F1 value of 93.15%, and mean average precision of 93.16%. Compared with other counting models, the proposed method exhibits a better comprehensive performance in terms of the counting accuracy, model size, and detection speed. Furthermore, when shrimp were counted within the entire culture area, the results were consistent with the true values.

3D visualization model construction based on generative adversarial networks.

The development of computer vision technology is rapid, which supports the automatic quality control of precision components efficiently and reliably. This paper focuses on the application of computer vision technology in manufacturing quality control. A new deep learning algorithm is presented, Multi-angle projective Generative Adversarial Networks (MapGANs), to automatically generate 3D visualization models of products and components. The generated 3D visualization models can intuitively and accurately display the product parameters and indicators. Based on these indicators, our model can accurately determine whether the product meets the standard. The working principle of the MapGANs algorithm is to automatically infer the basic three-dimensional shape distribution through the product’s projection module, while using multiple angles and multiple views to improve the fineness and accuracy of the three-dimensional visualization model. The experimental results prove that MapGANs can effectively reconstruct two-dimensional images into three-dimensional visualization models, and meanwhile accurately predict whether the quality of the product meets the standard.

A Lightweight Attention-Based Convolutional Neural Networks for Tomato Leaf Disease Classification

Plant diseases pose a significant challenge for food production and safety. Therefore, it is indispensable to correctly identify plant diseases for timely intervention to protect crops from massive losses. The application of computer vision technology in phytopathology has increased exponentially due to automatic and accurate disease detection capability. However, a deep convolutional neural network (CNN) requires high computational resources, limiting its portability. In this study, a lightweight convolutional neural network was designed by incorporating different attention modules to improve the performance of the models. The models were trained, validated, and tested using tomato leaf disease datasets split into an 8:1:1 ratio. The efficacy of the various attention modules in plant disease classification was compared in terms of the performance and computational complexity of the models. The performance of the models was evaluated using the standard classification accuracy metrics (precision, recall, and F1 score). The results showed that CNN with attention mechanism improved the interclass precision and recall, thus increasing the overall accuracy (>1.1%). Moreover, the lightweight model significantly reduced network parameters (~16 times) and complexity (~23 times) compared to the standard ResNet50 model. However, amongst the proposed lightweight models, the model with attention mechanism nominally increased the network complexity and parameters compared to the model without attention modules, thereby producing better detection accuracy. Although all the attention modules enhanced the performance of CNN, the convolutional block attention module (CBAM) was the best (average accuracy 99.69%), followed by the self-attention (SA) mechanism (average accuracy 99.34%).

An improved computation method for asphalt pavement texture depth based on multiocular vision 3D reconstruction technology

The depth of an asphalt pavement structure is an important index used to reflect the anti-skid performance of the pavement, which directly affects the driving safety of vehicles. In the current specifications (e.g., China, European and American standards), the mean texture depth (MTD) of an asphalt pavement is generally measured by the sand-patch method (SPM). However, SPM is easily affected by the operator’s subjective experience and experimental environment, resulting in low accuracy and high scatter in the data. In view of such shortcomings, a fast and accurate method to obtain the texture depth of asphalt pavement was proposed by designing a new test tool and a computer-aided calculation method. The multiocular vision theory was adopted to reconstruct a three-dimensional (3D) point cloud model of pavement texture. To reflect the concavity and convexity of pavement texture and eliminate the influence of pavement slope, the iterative closest point (ICP) algorithm was employed to obtain the datum reference plane (DRP). On this basis, the pentahedral volume calculation method suitable for evaluating the texture depth of an asphalt pavement was proposed. Laboratory experiment results using SPM were inverted by equal volume calculation to obtain the texture reference plane (TRP) and MTD'. Meanwhile, a 3D average maximum elevation (Avg.EL) algorithm was obtained by the evolution of two-dimensional mean profile depth (MPD). Furthermore, three parameters, namely MTD, MTD', and Avg.EL, were used to measure the texture depth of two field pavement sections in service. The experimental results showed that the correlation between MTD' and MTD was better than that between Avg.EL and MTD, and the degree of scatter was smaller. MTD' could be used directly as a reference value for MTD. In the present work, the 3D volume calculation method of asphalt pavement texture depth was proposed, which integrates the technical flow of image acquisition, model reconstruction, and algorithm analysis. The developed method provides a technical reference for the application of computer vision technology in the analysis of pavement texture depth.

Application of Computer Vision Technology in the Detection of Agricultural Products and Food

Application of Computer Vision Technology in the Detection of Agricultural Products and Food

A Rail Surface Defect Detection Method Based on Pyramid Feature and Lightweight Convolutional Neural Network

The application of computer vision technology in defect detection of industrial products is a popular research direction in recent years. This article presents the pyramid feature convolutional neural network (CNN) for defect detection of rail surfaces. First, multi-scale feature maps are extracted based on the characteristics of defects and backgrounds by the pyramid feature extraction module (PFEM). Then the feature maps are input to a lightweight network consisting of a small number of parameters. The network is trained with only 40% data of the dataset using binary cross-entropy loss function and the intersection of union (IOU) loss function. In the experiment, the performance of the proposed method is evaluated using the rail surface defect dataset (RSDD) dataset by comparing it with other methods. The experimental results show that the segmentation performance and real-time performance of the proposed method are better than those of other methods.

Research on the Application of Computer Vision Technology in Power System UAV Line Inspection

The stability of transmission lines is one of the important factors to ensure stable power supply. In this paper, a set of UAV intelligent power line inspection flight control system is designed according to the special flight requirements of UAV in power line inspection operation based on the principle of photogrammetry. The key technologies of the system include UAV flight attitude control, high-precision time synchronization between multiple sensors, defect detection and intelligent diagnosis, airborne sensor calibration, wireless communication, and ground data processing. In this paper, a camera and an on-board computer are innovatively used for real-time image processing and pattern recognition for wire feature acquisition. At the same time, this paper proposes an improved Canny edge detection algorithm combined with image segmentation technology to achieve wire feature extraction and identification. The field test results show that the system can complete the remote control of the UAV and realize the intelligent navigation and inspection of the transmission line UAV.

The Development and Application of Computer Vision Technology in The Era of Artificial Intelligence

With the increasing popularity and wide application of Internet technology, the computer field is in the stage of vigorous development, the world is moving towards the economic development period dominated by information industry. In recent years, with the birth of the emerging field of artificial intelligence, which solves problems that traditional computer technology can not solve, more and more people have a strong interest in it and devote themselves to this research, making artificial intelligence has evolved into a mainstream discipline in the field of computer. As one of the core technologies of artificial intelligence, computer vision has made remarkable progress in theoretical research and technical application, and has been widely used in home furnishing, medical treatment, network and security. This paper mainly introduces the concept and relationship between artificial intelligence and computer vision, discusses the main technology and application scenarios of computer vision, and proposes a vision for the future development.