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Power Electronics, Computer applications (ICPECA), IEEE International Conference on

作者： Quanrong Fang Amazon (China) Investment Co. Ltd Beijing China

In various fields such as medical imaging, object detection, and video surveillance, multi view natural language query systems utilize image data to provide a more comprehensive perspective, allowing users to intuitively query and obtain information. Due to the lack of a deep understanding of natural language in the hard coded matching rule method, the query results do not match the user's intentions and are difficult to meet practical application needs. Therefore, this article introduces machine vision algorithms for optimization and improvement. This article first discusses the system architecture of four modules: data input and preprocessing, visual feature extraction, natural language understanding and matching, and result generation and feedback. Then, the application of machine vision technology in the system was analyzed using two calculation formulas: grayscale conversion and binarization, and natural language processing technology was briefly discussed. Subsequently, a context understanding module was added to construct a multi view natural language query system based on machine vision. Finally, two sets of simulation experiments were conducted to draw the following conclusion: compared with traditional methods, the overall average improvement in image recognition accuracy indicators is about 14.3%, while the overall average improvement in response speed indicators is about 26.5%. This research system can effectively process images from different perspectives and match them with natural language queries.

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Authentication and Verification in Human-Robot Cooperative Robotic Cells using Stereo vision and Gesture Control

Authentication and Verification in Human-Robot Cooperative R...

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International image processing, applications and Systems Conference (IPAS)

作者： Gábor Kovács Tamás Szirányi Machine Perception Research Laboratory HUN-REN Institute for Computer Science and Control Budapest Hungary

ISBN: (数字)9798331506520

ISBN: (纸本)9798331506537

The integration of human-robot interaction (HRI) technologies with industrial automation has become increasingly essential for enhancing productivity and safety in manufacturing environments. In this paper, we propose a novel approach to address these challenges by using stereo vision and gesture control in cooperative robotic cells. Our system enables seamless authentication of operators and real-time verification of task execution, ensuring compliance with established protocols and safety *** features of our system include its gesture-based operation with gesture recognition algorithms, allowing operators to interact with robotic systems intuitively and efficiently. By leveraging stereo vision, our system accurately tracks the operators’ movement within the workspace, facilitating precise task execution and object *** present a detailed description of our system architecture, experimental configuration, and real-world performance assessment. Our results demonstrate the effectiveness and feasibility of our approach in enhancing operational efficiency, ensuring quality, and improving the overall user experience in industrial automation.

关键词： Automation Service robots Tracking Human-robot interaction Authentication Systems architecture User experience Real-time systems Stereo vision Safety

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Simulation Research and Development of Agricultural Products processing Process Based on machine Learning Algorithm

Simulation Research and Development of Agricultural Products...

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2022 International Conference on Artificial Intelligence and Autonomous Robot Systems, AIARS 2022

作者： Peng, Luo Shihezi University School of Food Science and Technology Xinjiang Shihezi832003 China

ISBN: (数字)9781665454575

ISBN: (纸本)9781665454575

In recent years, with the development of science and technology and its application in agricultural production, China's agricultural science and technology have made great progress, the concept of "agricultural processing"has been mentioned, and the research on agricultural processing has also achieved fruitful results. The combination of intelligent and automated machine learning algorithms with traditional industries can promote productivity improvement on the one hand, and realize industrial upgrading and transformation on the other hand. However, in practical production applications, machine learning algorithms are restricted by factors such as high cost, and the research and application of machine learning algorithms are greatly limited. With the development of virtual simulation technology in the field of machine learning algorithm research, it provides a new way for machine learning algorithm technology to be applied to agricultural product processing. Therefore, the research on machine learning algorithms has become a trend. The development of machine learning algorithms will drive the development of modern agriculture. It is very necessary for the research of machine learning algorithms to learn algorithms. The image is converted into a data matrix, and a computer used to replace the human brain is used to analyze the image, while completing a vision related task. China's agricultural development is facing severe challenges such as rising costs, continuous deterioration of the ecological environment and high tension of resource conditions. With the deepening of machine learning algorithm research and the rapid development of machine learning algorithm technology, machine learning algorithm simulation technology, as a safe and economic experimental tool in the application of machine learning algorithm technology, plays a more and more important role. In order to make full use of the latest research results abroad and narrow the gap with the advanced level ab

关键词： Deterioration

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Self-Adaptive Logit Balancing for Deep Learning Robustness in Computer vision 21st

Self-Adaptive Logit Balancing for Deep Learning Robustness i...

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21st International Conference on image Analysis and processing (ICIAP)

作者： Wei, Jiefei Meng, Qinggang Yao, Luyan Loughborough Univ Epinal Way Loughborough LE11 3TU Leics England Univ Nottingham Univ Pk Nottingham NG7 2RD England

ISBN: (纸本)9783031064272;9783031064265

With wide applications of machine learning algorithms, machine learning security has become a significant issue. The vulnerability to adversarial perturbations exists in most machine learning algorithms, including cutting-edge deep neural networks. The standard adversarial perturbation defence techniques with adversarial training need to generate adversarial examples during the training process, which require high computational costs. This paper proposed a novel defence method using self-adaptive logit balancing and Gaussian noise boost training. This method can improve the robustness of deep neural networks without high computational cost and achieve competitive results compared with the adversarial training methods. Meanwhile, this defence method enables deep learning systems to have proactive and reactive defence during the operation. A sub-classifier is trained to determine whether the system is under attack and detect attack algorithms via the patterns of the Log-Softmax values. It can achieve high accuracy for detecting clean inputs and adversarial examples created by seven attack methods.

关键词： machine learning security Adversarial robustness Adversarial examples Deep neural networks

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PCASNet: Polarized Cross-scale Attention Self-attention Network for Lightweight Ultrasound Medical image Segmentation

PCASNet: Polarized Cross-scale Attention Self-attention Netw...

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image processing, Computer vision and machine Learning (ICICML), International Conference on

作者： Qingxue Zhao Di Wu Jun Tian College Of Software Nankai University Tianjin China

ISBN: (数字)9798350355413

ISBN: (纸本)9798350355420

U-Net and its extensions have achieved significant success in medical image segmentation but face limitations in capturing global context and transferring cross-scale features. To address these challenges, we propose a lightweight network, PCASNet, which integrates the Polarized Cross-scale Attention Module (PCAS Module) and the Dynamic Multi-scale Attention Gate (DMAG). The PCAS Module enhances global context modeling by fusing features from distant spatial positions, while the DMAG improves segmentation performance by filtering redundant features and emphasizing critical information, thereby strengthening global information modeling and feature selection capabilities. Experiments on breast and thyroid ultrasound datasets demonstrate that PCASNet outperforms traditional image segmentation algorithms in both accuracy and efficiency, highlighting its potential for applications in ultrasound medical imaging.

关键词： image segmentation Ultrasonic imaging Computational modeling Computer architecture Logic gates Convolutional neural networks Context modeling Biomedical imaging Principal component analysis Thyroid

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Harnessing Deep Learning Techniques for Lung Disease Diagnosis: A Comparative Study of CNN, Transformer and Mamba Models

Harnessing Deep Learning Techniques for Lung Disease Diagnos...

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image processing, Computer vision and machine Learning (ICICML), International Conference on

作者： Chenkai Tang Leicester International Institute Dalian University of Technology Panjin China

ISBN: (数字)9798350355413

ISBN: (纸本)9798350355420

Pneumonia, an infectious lung condition caused by bacteria, viruses, or other microorganisms, significantly impacts both pediatric and geriatric populations. The COVID-19 pandemic has underscored the necessity for swift and accurate diagnostic tools, with over 760 million infections reported. This study investigates the role of artificial intelligence in lung X-ray image classification by comparing the performance of three Convolutional Neural Networks, two Transformer models, and the vision Mamba model using a standardized dataset. The study’s objectives were to (1) identify the most effective models, (2) examine the impact of initializing models using transfer learning, and (3) evaluate the vision Mamba model’s performance relative to conventional models. Using a Kaggle dataset containing four X-ray image categories—COVID-19, normal, lung opacity, and viral pneumonia—six models were trained and tested with and without transfer learning. Results indicated that the Swin Transformer model outperformed others, and transfer learning significantly enhanced model accuracy. Although the vision Mamba model exhibited lower accuracy, its computational efficiency highlights its potential utility. This study provides critical insights into artificial intelligence applications in lung disease diagnosis, supporting clinicians with accurate diagnostic tools.

关键词： Deep learning Pneumonia Accuracy Computational modeling Lungs Transfer learning Transformers Convolutional neural networks X-ray imaging image classification

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Online inspection of narrow overlap weld quality using two-stage convolution neural network image recognition

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machine vision AND applications 2021年第1期32卷 p1-22页

作者： Miao, Rui Jiang, Zihang Zhou, Qinye Wu, Yizhou Gao, Yuntian Zhang, Jie Jiang, Zhibin Shanghai Jiao Tong Univ Sch Naval Architecture Ocean & Civil Engn Shanghai 200240 Peoples R China Shanghai Jiao Tong Univ Sch Mech Engn Shanghai 200240 Peoples R China Shanghai Jiao Tong Univ SJTU ParisTech Elite Inst Technol Shanghai 200240 Peoples R China Donghua Univ Inst Artificial Intelligence Shanghai 201620 Peoples R China Shanghai Jiao Tong Univ Antai Coll Econ & Management Shanghai 200240 Peoples R China

In narrow overlap welding, serious defects in the weld will lead to band breakage accident, and the whole hot dip galvanizing unit will be shut down. Laser vision inspection hardware is used to collect real-time image of weld surface, and image defect recognition and evaluation system is developed to real-time detect quality. Firstly, region division is implemented. In view of the characteristics of gray image such as large information, low contrast and blurred edge, an improved image segmentation algorithm is proposed by using image convolution to enhance edge features and combining with integral image, which can quickly and accurately extract the weld edge and divide the region, and the processing time can meet the real-time requirements. Then comparing the effect of traditional method and convolution neural network in identifying defects, VGG16 is used to identify weld defects. In order to ensure real-time performance, a two-stage weld defect recognition is proposed. First, the large defective area is identified, and then, the defect is accurately identified in the area. This method can quickly extract defect areas and complete weld defect classification. Experiments show that the accuracy can reach 97% and average running time takes 3.2 s, meeting the online detection requirements.

关键词： Narrow lap welding Surface defects image processing Convolutional neural network

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Surveying image segmentation approaches in astronomy

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ASTRONOMY AND COMPUTING 2024年 48卷

作者： Xu, D. Zhu, Y. Univ Virginia Dept Astron 530 Mocormick Rd Charlottesville VA 22904 USA Princeton Univ Dept Comp Sci 35 Olden St Princeton NJ 08540 USA

image segmentation plays a critical role in unlocking the mysteries of the universe, providing astronomers with a clearer perspective on celestial objects within complex astronomical images and data cubes. Manual segmentation, while traditional, is not only time-consuming but also susceptible to biases introduced by human intervention. As a result, automated segmentation methods have become essential for achieving robust and consistent results in astronomical studies. This review begins by summarizing traditional and classical segmentation methods widely used in astronomical tasks. Despite the significant improvements these methods have brought to segmentation outcomes, they fail to meet astronomers' expectations, requiring additional human correction, further intensifying the labor-intensive nature of the segmentation process. The review then focuses on the transformative impact of machine learning, particularly deep learning, on segmentation tasks in astronomy. It introduces state-of-the-art machine learning approaches, highlighting their applications and the remarkable advancements they bring to segmentation accuracy in both astronomical images and data cubes. As the field of machine learning continues to evolve rapidly, it is anticipated that astronomers will increasingly leverage these sophisticated techniques to enhance segmentation tasks in their research projects. In essence, this review serves as a comprehensive guide to the evolution of segmentation methods in astronomy, emphasizing the transition from classical approaches to cutting -edge machine learning methodologies. We encourage astronomers to embrace these advancements, fostering a more streamlined and accurate segmentation process that aligns with the ever-expanding frontiers of astronomical exploration.

关键词： Segmentation machine learning Neural network vision Transformer Generative model Astronomy image processing

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Detection and Identification of Digital Display Meter of Distribution Cabinet Based on YOLOv5 Algorithm 3rd

Detection and Identification of Digital Display Meter of Dis...

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3rd International Conference on Neural Computing for Advanced applications, NCAA 2022

作者： Zhou, Yanfei Zhang, Yunchu Wang, Chao Sun, Shaohan Wang, Jimin Shandong Jianzhu University Jinan250101 China Shandong Key Laboratory of Intelligent Buildings Technology Jinan250101 China

ISBN: (纸本)9789811961410

Aiming at the problem of low recognition accuracy of digital display meter readings when the inspection robot performs inspection tasks, a YOLOv5-based digital display meter detection and recognition algorithm for distribution cabinets is proposed. For the digital display meter image captured by the inspection robot spherical camera, the YOLOv5 model is used to locate the target character area. After scale normalization, image correction, filtering and noise removal and other image pre-processing operations, the character recognition is completed in combination with the traditional machine vision algorithm, and the reading results are automatically output. For the characteristics of digital tube characters, the threading method, support vector machine algorithm and PaddleOCR algorithm are used for comparison, and a suitable algorithm model is selected to recognize numeric, alphabetic and decimal point characters. The experimental results show that the accuracy of detecting and identifying digital display meters using the YOLOv5 model and PaddleOCR algorithm is 95.3%. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Computer vision

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Advanced Object Detection and Pose Estimation with Hybrid Task Cascade and High-Resolution Networks

Advanced Object Detection and Pose Estimation with Hybrid Ta...

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image processing, Computer vision and machine Learning (ICICML), International Conference on

作者： Yuhui Jin Yaqiong Zhang Zheyuan Xu Wenqing Zhang Jingyu Xu California Institute of Technology Pasadena USA University of Michigan Ann Arbor Ann Arbor USA University of Washington Sunnyvale USA Washington University in St. Louis Louis USA Northern Arizona University Arizona USA

ISBN: (数字)9798350355413

ISBN: (纸本)9798350355420

In the field of computer vision, 6D object detection and pose estimation are critical for applications such as robotics, augmented reality, and autonomous driving. Traditional methods often struggle with achieving high accuracy in both object detection and precise pose estimation simultaneously. This study proposes an improved 6D object detection and pose estimation pipeline based on the existing 6D-VNet framework, enhanced by integrating a Hybrid Task Cascade (HTC) and a High-Resolution Network (HRNet) backbone. By leveraging the strengths of HTC’s multi-stage refinement process and HRNet’s ability to maintain high-resolution representations, our approach significantly improves detection accuracy and pose estimation precision. Furthermore, we introduce advanced post-processing techniques and a novel model integration strategy that collectively contribute to superior performance on public and private benchmarks. Our method demonstrates substantial improvements over state-of-the-art models, making it a valuable contribution to the domain of 6D object detection and pose estimation.

关键词： Computer vision Accuracy image processing Pose estimation Pipelines Object detection Benchmark testing Robustness Autonomous vehicles Augmented reality

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