Computer Vision Applications Research Articles

Enhancing machine learning recommendation systems with CNN architectures: Applications and innovations in computer vision and speech recognition

Abstract. This paper provides an in-depth analysis of enhancing machine learning recommendation systems using Convolutional Neural Network (CNN) architectures, with a focus on their applications in computer vision and speech recognition. Traditional recommendation systems often struggle with scalability and the complexity of high-dimensional data. By integrating CNNs, these systems can utilize advanced feature extraction and representation learning to more effectively process and analyze diverse data sources. Our study demonstrates significant improvements in recommendation accuracy and personalization through CNN-enhanced systems. We discuss the architecture, design principles, and advantages of CNNs, supported by case studies across various domains. Our findings illustrate the potential of CNNs to revolutionize recommendation systems by addressing existing limitations and offering innovative solutions for real-time, high-quality recommendations. This research emphasizes the importance of advanced machine learning techniques in creating robust and scalable recommendation systems, paving the way for future advancements and applications in multiple fields.

Computer vision applications for the detection or analysis of tuberculosis using digitised human lung tissue images - a systematic review

ObjectiveTo conduct a systematic review of the computer vision applications that detect, diagnose, or analyse tuberculosis (TB) pathology or bacilli using digitised human lung tissue images either through automatic or semi-automatic methods. We categorised the computer vision platform into four technologies: image processing, object/pattern recognition, computer graphics, and deep learning. In this paper, the focus is on image processing and deep learning (DL) applications for either 2D or 3D digitised human lung tissue images. This review is useful for establishing a common practice in TB analysis using human lung tissue as well as identifying opportunities for further research in this space. The review brings attention to the state-of-art techniques for detecting TB, with emphasis on the challenges and limitations of the current techniques. The ultimate goal is to promote the development of more efficient and accurate algorithms for the detection or analysis of TB, and raise awareness about the importance of early detection.DesignWe searched five databases and Google Scholar for articles published between January 2017 and December 2022 that focus on Mycobacterium tuberculosis detection, or tuberculosis pathology using digitised human lung tissue images. Details regarding design, image processing and computer-aided techniques, deep learning models, and datasets were collected and summarised. Discussions, analysis, and comparisons of state-of-the-art methods are provided to help guide future research. Further, a brief update on the relevant techniques and their performance is provided.ResultsSeveral studies have been conducted to develop automated and AI-assisted methods for diagnosing Mtb and TB pathology from digitised human lung tissue images. Some studies presented a completely automated method of diagnosis, while other studies developed AI-assisted diagnostic methods. Low-level focus areas included the development of a novel μ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\upmu$$\\end{document}CT scanner for soft tissue image contract, and use of multiresolution computed tomography to analyse the 3D structure of the human lung. High-level focus areas included the investigation the effects of aging on the number and size of small airways in the lungs using CT and whole lung high-resolution μ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\upmu$$\\end{document}CT, and the 3D microanatomy characterisation of human tuberculosis lung using μ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\upmu$$\\end{document}CT in conjunction with histology and immunohistochemistry. Additionally, a novel method for acquiring high-resolution 3D images of human lung structure and topology is also presented.ConclusionThe literature indicates that post 1950s, TB was predominantly studied using animal models even though no animal model reflects the full spectrum of human pulmonary TB disease and does not reproducibly transmit Mtb infection to other animals (Hunter, 2011). This explains why there are very few studies that used human lung tissue for detection or analysis of Mtb. Nonetheless, we found 10 studies that used human tissues (predominately lung) of which five studies proposed machine learning (ML) models for the detection of bacilli and the other five used CT on human lung tissue scanned ex-vivo.

Towards industry-ready additive manufacturing: AI-enabled closed-loop control for 3D melt electrowriting.

Melt electrowriting (MEW) is an emerging high-resolution 3D printing technology used in biomedical engineering, regenerative medicine, and soft robotics. Its transition from academia to industry faces challenges such as slow experimentation, low printing throughput, poor reproducibility, and user-dependent operation, largely due to the nonlinear and multiparametric nature of the MEW process. To address these challenges, we applied computer vision and machine learning to monitor and analyze the process in real-time through imaging of the MEW jet between the nozzle-collector gap. To collect data for training we developed an automated data collection methodology that eases the experimental time from days to hours. A feedforward neural network, working in concert with optimization methods and a feedback loop, is used to develop closed-loop control ensuring reproducibility of the printed parts. We demonstrate that machine learning allows streamlining the MEW operation via closed-loop control of the highly nonlinear 3D printing technology.

Enhanced Edge Detection through Binary Particle Swarm Optimization and L0 Guided Filtering

Detecting edges holds significant importance in image processing, serving as a fundamental step in numerous computer vision applications. This paper presents an innovative method for performing edge detection by combining Binary Particle Swarm Optimization (BPSO) with L0 Guided Filtering. The proposed method aims to address the challenge of accurately detecting edges in noisy and complex images by leveraging the benefits of both BPSO and L0 guided filtering. The process begins with the initialization of the BPSO algorithm, where binary particles traverse the solution space to optimize parameters critical for edge detection. These optimized parameters are subsequently employed in the L0 guided filtering framework, a sophisticated edge preserving filter known for its ability to maintain fine details while effectively reducing noise. The synergy of BPSO and L0 guided filtering demonstrates improved adaptability to diverse image characteristics, enhancing the overall robustness of edge detection. The binary nature of BPSO allows for efficient exploration of the solution space, facilitating faster convergence to optimal parameters. Concurrently, the L0 guided filtering ensures edge preserving smoothing, contributing to the suppression of unwanted artifacts. Experimental evaluations on benchmark datasets showcase the effectiveness of the proposed method compared to traditional edge detection techniques. The results indicate superior edge localization and reduced sensitivity to noise, highlighting the potential of the BPSO Based Edge Detection under L0 Guided Filtering in real world applications. The presented approach offers a valuable contribution to the advancement of edge detection methodologies, demonstrating its potential for enhancing the performance of computer vision systems in various domains.

Current application of artificial intelligence in laparoscopic cholecystectomy

Recent advances in artificial intelligence (AI) have sparked a surge in the application of computer vision (CV) in surgical video analysis. Surgical complications often occur due to lapses in judgment and decision-making. In laparoscopic cholecystectomy, achievement of the critical view of safety is commonly advocated to prevent bile duct injuries. However, bile duct injuries rates remain stable, probably due to inconsistent application or a poor understanding of critical view of safety. Advances in AI have made it possible to train algorithms that identify anatomy and interpret the surgical field. AI-based CV techniques may leverage surgical video data to develop real-time automated decision support tools and surgeon training systems. The effectiveness of CV application in surgical procedures is still under early evaluation. The review considers the commonly used deep learning algorithms in CV and describes their usage in detail in four application scenes, including phase recognition, anatomy detection, instrument detection and action recognition in laparoscopic cholecystectomy. The MedLine, Scopus, and IEEE Xplore databases were searched for publications up to 2024. The keywords used in the search were “laparoscopic cholecystectomy”, “artificial intelligence”. The currently described applications of CV in laparoscopic cholecystectomy are limited. Most current research focus on the identification of workflow and anatomical structure, while the identification of instruments and surgical actions is still awaiting further breakthroughs. Future research on the use of CV in laparoscopic cholecystectomy should focus on application in more scenarios, such as surgeon skill assessment and the development of more efficient models.

A Staged Framework for Computer Vision Education: Integrating AI, Data Science, and Computational Thinking

Computer vision education is increasingly important in modern technology curricula; yet, it often lacks a systematic approach integrating both theoretical concepts and practical applications. This study proposes a staged framework for computer vision education designed to progressively build learners’ competencies across four levels. This study proposes a four-staged framework for computer vision education, progressively introducing concepts from basic image recognition to advanced video analysis. Validity assessments were conducted twice with 25 experts in the field of AI education and curricula. The results indicated high validity of the staged framework. Additionally, a pilot program, applying computer vision to acid–base titration activities, was implemented with 40 upper secondary school students to evaluate the effectiveness of the staged framework. The pilot program showed significant improvements in students’ understanding and interest in both computer vision and scientific inquiry. This research contributes to the AI educational field by offering a structured, adaptable approach to computer vision education, integrating AI, data science, and computational thinking. It provides educators with a structured guide for implementing progressive, hands-on learning experiences in computer vision, while also highlighting areas for future research and improvement in educational methodologies.

A Candy Defect Detection Method Based on StyleGAN2 and Improved YOLOv7 for Imbalanced Data.

Quality management in the candy industry is a vital part of food quality management. Defective candies significantly affect subsequent packaging and consumption, impacting the efficiency of candy manufacturers and the consumer experience. However, challenges exist in candy defect detection on food production lines due to the small size of the targets and defects, as well as the difficulty of batch sampling defects from automated production lines. A high-precision candy defect detection method based on deep learning is proposed in this paper. Initially, pseudo-defective candy images are generated based on Style Generative Adversarial Network-v2 (StyleGAN2), thereby enhancing the authenticity of these synthetic defect images. Following the separation of the background based on the color characteristics of the defective candies on the conveyor belt, a GAN is utilized for negative sample data enhancement. This effectively reduces the impact of data imbalance between complete and defective candies on the model's detection performance. Secondly, considering the challenges brought by the small size and random shape of candy defects to target detection, the efficient target detection method YOLOv7 is improved. The Spatial Pyramid Pooling Fast Cross Stage Partial Connection (SPPFCSPC) module, the C3C2 module, and the global attention mechanism are introduced to enhance feature extraction precision. The improved model achieves a 3.0% increase in recognition accuracy and a 3.7% increase in recall rate while supporting real-time recognition scenery. This method not only enhances the efficiency of food quality management but also promotes the application of computer vision and deep learning in industrial production.

Application of Computer Vision and Lego Mindstorms in Measuring Brewster’s Angle and Refractive Index

Application of Computer Vision and Lego Mindstorms in Measuring Brewster’s Angle and Refractive Index

Synthetic Image Data Generation via Rendering Techniques for Training AI-Based Instance Segmentation

Synthetic image data generation has gained popularity in computer vision and machine learning in recent years. The work introduces a technique for creating artificial image data by utilizing 3D files and rendering methods in Python and Blender. The technique employs BlenderProc, a rendering tool for generating artificial images, to efficiently create a substantial amount of data. The output of the method is saved in JSON format, containing COCO annotations of objects in the images, facilitating seamless integration with current machine-learning pipelines. The paper shows that the created synthetic data can be used to enhance object data during simulation. The method can enhance the accuracy and robustness of machine-learning models by modifying simulation parameters like lighting, camera position, and object orientation to create a variety of images. This is especially beneficial for applications that require significant amounts of labelled real-world data, which can be time-consuming and labour-intensive to obtain. The study addresses the constraints and potential prejudices of creating synthetic data, emphasizing the significance of verifying and assessing the generated data prior to its utilization in machine learning models. Synthetic data generation can be a valuable tool for improving the efficiency and effectiveness of machine learning and computer vision applications. However, it is crucial to thoroughly assess the potential limitations and biases of the generated data. This paper emphasizes the potential of synthetic data generation to enhance the accuracy and resilience of machine learning models, especially in scenarios with limited access to labelled real-world data. This paper introduces a method that efficiently produces substantial amounts of synthetic image data with COCO annotations, serving as a valuable resource for professionals in computer vision and machine learning.

Enhanced Vehicle Logo Detection Method Based on Self-Attention Mechanism for Electric Vehicle Application

Vehicle logo detection plays a crucial role in various computer vision applications, such as vehicle classification and detection. In this research, we propose an improved vehicle logo detection method leveraging the self-attention mechanism. Our feature-sampling structure integrates multiple attention mechanisms and bidirectional feature aggregation to enhance the discriminative power of the detection model. Specifically, we introduce the multi-head attention for multi-scale feature fusion module to capture multi-scale contextual information effectively. Moreover, we incorporate the bidirectional aggregation mechanism to facilitate information exchange between different layers of the detection network. Experimental results on a benchmark dataset (VLD-45 dataset) demonstrate that our proposed method outperforms baseline models in terms of both detection accuracy and efficiency. Our experimental evaluation using the VLD-45 dataset achieves a state-of-the-art result of 90.3% mAP. Our method has also improved AP by 10% for difficult samples, such as HAVAL and LAND ROVER. Our method provides a new detection framework for small-size objects, with potential applications in various fields.

Deep models for multi-view 3D object recognition: a review

This review paper focuses on the progress of deep learning-based methods for multi-view 3D object recognition. It covers the state-of-the-art techniques in this field, specifically those that utilize 3D multi-view data as input representation. The paper provides a comprehensive analysis of the pipeline for deep learning-based multi-view 3D object recognition, including the various techniques employed at each stage. It also presents the latest developments in CNN-based and transformer-based models for multi-view 3D object recognition. The review discusses existing models in detail, including the datasets, camera configurations, view selection strategies, pre-trained CNN architectures, fusion strategies, and recognition performance. Additionally, it examines various computer vision applications that use multi-view classification. Finally, it highlights future directions, factors impacting recognition performance, and trends for the development of multi-view 3D object recognition method.

OptiRet-Net: An Optimized Low-Light Image Enhancement Technique for CV-based Applications in Resource-Constrained Environments

The illumination of images can significantly impact computer-vision applications such as image classification, multiple object detection, and tracking, leading to a significant decline in detection and tracking accuracy. Recent advancements in deep learning techniques have been applied to low-light image enhancement (LLIE) to combat this issue. Retinex theory-based methods following a decomposition-adjustment pipeline for LLIE have performed well in various aspects. Despite their success, current research on Retinex-based deep learning still needs to improve in terms of optimization techniques and complicated convolution connections, which can be computationally intensive for end-device deployment. We propose an Optimized Retinex-based CNN (OptiRet-Net) deep-learning framework to address these challenges for the low-light image enhancement problem. Our results demonstrate that the proposed method outperforms existing state-of-the-art models in terms of full reference metrics with a PSNR of 21.87, SSIM of 0.80, LPIPS of 0.16, and zero reference metrics with a NIQE of 3.4 and PIQE of 56.6. Additionally, we validate our approach using a comprehensive evaluation comprising five datasets and nine prior methods. Furthermore, we assess the efficacy of our proposed model combining low-light multiple object tracking applications using YOLOX and ByteTrack in versatile video coding (VVC/H.266) across various quantization parameters. Our findings reveal that LLIE-enhanced frames surpass their tracking results with a MOTA of 80.6% and a remarkable precision rate of 96%. Our model also achieves minimal file sizes by effectively compressing the enhanced low-light images while maintaining their quality, making it suitable for resource-constrained environments where storage or bandwidth limitations are a concern.

Reduction of Misclassifications in Wildfire Detection: A Weighted Ensemble Deep Learning Approach

Governments worldwide are increasingly prioritizing early wildfire detection to safeguard lives, property, and the environment. Although CNN-based models have demonstrated exceptional performance in various computer vision applications, the evolving nature of wildfire images poses significant challenges for a single CNN-based model in wildfire detection. In this study, we addressed this issue by integrating and weighting the differential learning capabilities of three individual transfer learning models: InceptionV3, ResNet50, and VGG16. Experimental results show that the ensemble deep learning models significantly outperformed all single classifiers across all performance metrics. Both the ensemble and weighted ensemble deep learning models achieved 99.7% accuracy, 99.5% precision, 100% recall, 99.8% F1-score, 0.5%false positive rate, 0.0% false negative rate and 0.3% error rate. Additionally, these models reduced the error rate by 98%, 91%, and 40% compared to the error rates of ResNet50, InceptionV3, and VGG16 respectively. A false negative rate of 0% indicates that our proposed ensemble deep learning models identified and predicted all the wildfire instances present in the test set correctly without a single misclassification. This positions our proposed ensemble deep learning models as superior choices for reducing misclassifications in wildfire detection.

Accelerated Data Engine: A faster dataset construction workflow for computer vision applications in commercial livestock farms

Large-scale, high-quality dataset was the foundation of developing advanced artificial intelligence applications. However, creating such a benchmark dataset in a professional field, such as precision management of animals, was always a challenge because of the costly and labor-intensive process of annotation and review. This study introduced a novel workflow named Accelerated Data Engine (ADE), designed to efficiently produce representative and high-quality computer vision datasets from raw animal surveillance footage. By incorporating referring and grounding models (R&G models) as auto-annotators, along with a distillation mechanism for dataset-auditors, ADE significantly speeded up the dataset construction process. The new workflow received natural language inputs as referrals to identify animal instances, delineated their body shapes, and then refined the auto-annotated data through a selection process. To demonstrate the efficacy of ADE, three 30-minute surveillance video samples featuring pigs, sheep, and cattle were discussed in this study. The results indicated the R&G models effectively annotated animals across various farms, while distillation mechanisms could identify various detection errors, balance the data representations, refine annotations, and verify the data quality. Two high-quality cattle datasets (6.5 k and 486 frames), including 26 k and 2.5 k cattle instances, were generated through the ADE workflow from 24-hour surveillance videos on a commercial cattle farm and made publicly available. The proposed dataset has achievable performance between 74.6 %∼84.1 %. The ADE workflow saved 78.4 % of manual work compared to the traditional dataset construction workflow (approximately 141 h). This pioneering approach empowered the fast creation of benchmark animal datasets and would enhance computer vision applications in the livestock production industry in the future.

Ensemble of vision transformer architectures for efficient Alzheimer’s Disease classification

Transformers have dominated the landscape of Natural Language Processing (NLP) and revolutionalized generative AI applications. Vision Transformers (VT) have recently become a new state-of-the-art for computer vision applications. Motivated by the success of VTs in capturing short and long-range dependencies and their ability to handle class imbalance, this paper proposes an ensemble framework of VTs for the efficient classification of Alzheimer’s Disease (AD). The framework consists of four vanilla VTs, and ensembles formed using hard and soft-voting approaches. The proposed model was tested using two popular AD datasets: OASIS and ADNI. The ADNI dataset was employed to assess the models’ efficacy under imbalanced and data-scarce conditions. The ensemble of VT saw an improvement of around 2% compared to individual models. Furthermore, the results are compared with state-of-the-art and custom-built Convolutional Neural Network (CNN) architectures and Machine Learning (ML) models under varying data conditions. The experimental results demonstrated an overall performance gain of 4.14% and 4.72% accuracy over the ML and CNN algorithms, respectively. The study has also identified specific limitations and proposes avenues for future research. The codes used in the study are made publicly available.

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.

A Spatial-Channel-Temporal-Fused Attention for Spiking Neural Networks.

Spiking neural networks (SNNs) mimic brain computational strategies, and exhibit substantial capabilities in spatiotemporal information processing. As an essential factor for human perception, visual attention refers to the dynamic process for selecting salient regions in biological vision systems. Although visual attention mechanisms have achieved great success in computer vision applications, they are rarely introduced into SNNs. Inspired by experimental observations on predictive attentional remapping, we propose a new spatial-channel-temporal-fused attention (SCTFA) module that can guide SNNs to efficiently capture underlying target regions by utilizing accumulated historical spatial-channel information in the present study. Through a systematic evaluation on three event stream datasets (DVS Gesture, SL-Animals-DVS, and MNIST-DVS), we demonstrate that the SNN with the SCTFA module (SCTFA-SNN) not only significantly outperforms the baseline SNN (BL-SNN) and two other SNN models with degenerated attention modules, but also achieves competitive accuracy with the existing state-of-the-art (SOTA) methods. Additionally, our detailed analysis shows that the proposed SCTFA-SNN model has strong robustness to noise and outstanding stability when faced with incomplete data, while maintaining acceptable complexity and efficiency. Overall, these findings indicate that incorporating appropriate cognitive mechanisms of the brain may provide a promising approach to elevate the capabilities of SNNs.

Enhancing industrial IoT with edge computing and computer vision: An analog gauge visual digitization approach

The advent of Industry 4.0 has led to the widespread adoption of Industrial Internet of Things (IIoT) and Computer Vision technologies in manufacturing. However, handling the massive data generated by IIoT poses challenges. To address this, we propose a software and hardware architecture that combines edge computing with IIoT to enable computer vision applications on manufacturing floors. This research contributes to integrating edge computing and IIoT with Computer Vision technologies in manufacturing facilities, offering a cost-effective and scalable solution for machine vision applications. This approach benefits Small and Medium Manufacturers (SMMs) by utilizing low-cost hardware and open-source technologies, enabling them to harness Industry 4.0 advantages. Our approach involves IoT devices using Raspberry Pi microcomputers and USB webcams as clients and a Python-based edge node server software as the central component. Our test case study results show that a centralized edge-computing solution can offer several useability benefits and a significant performance increase over using a non-edge-enabled IIoT approach. The proposed architecture is tested, validated, and verified through a case study focused on the digitization of the readings of analog gauges. Our experimentation results can offer valuable insights and guidance for the broader adoption of edge-enabled IIoT solutions in manufacturing contexts.

Transformers-based architectures for stroke segmentation: a review

Stroke remains a significant global health concern, necessitating precise and efficient diagnostic tools for timely intervention and improved patient outcomes. The emergence of deep learning methodologies has transformed the landscape of medical image analysis. Recently, Transformers, initially designed for natural language processing, have exhibited remarkable capabilities in various computer vision applications, including medical image analysis. This comprehensive review aims to provide an in-depth exploration of the cutting-edge Transformer-based architectures applied in the context of stroke segmentation. It commences with an exploration of stroke pathology, imaging modalities, and the challenges associated with accurate diagnosis and segmentation. Subsequently, the review delves into the fundamental ideas of Transformers, offering detailed insights into their architectural intricacies and the underlying mechanisms that empower them to effectively capture complex spatial information within medical images. The existing literature is systematically categorized and analyzed, discussing various approaches that leverage Transformers for stroke segmentation. A critical assessment is provided, highlighting the strengths and limitations of these methods, including considerations of performance and computational efficiency. Additionally, this review explores potential avenues for future research and development.

Technology innovation in library: a bibliometric review

Technological innovation is changing libraries and how libraries operate. This study presents a bibliometric analysis to identify key patterns and trends in research on technological innovation in libraries, based on the publication of papers indexed in Web of Science from 2019 to 2023. A total of 433 documents from 342 sources were analyzed using Biblioshiny and VOSViewer software. The results show an increasing interest in this research domain, despite a negative annual publication growth rate of -32.33% in recent years. The IEEE Winter Conference on Computer Vision Applications and the IEEE Symposium on Security and Privacy were the most productive sources. "Library" was the most frequent keyword found in 6% of documents, along with terms such as "model", "performance", and "system", highlighting the focus on developing and evaluating technological systems for libraries. The findings summarize the key topics, trends, and leading countries driving technology innovation research for libraries over the past five years. This bibliometric review provides insights to guide future research efforts in this area.