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Design of intelligent security robot based on machine vision

Design of intelligent security robot based on machine vision

引用

2023 International conference on Algorithm, Imaging processing, and machine vision, AIPMV 2023

作者： Huang, Xueying Chang, Zhoulin College of Mechanical and Electrical Engineering Guangdong University of Science and Technology Guangdong Dongguan China

ISBN: (纸本)9781510672444

With the continuous improvement of the application of artificial intelligence theory and intelligent hardware processing capabilities, the application of machine vision and unmanned technology is becoming more and more popular. As the city scales rapidly, fire safety issues are becoming more and more important. Taking into account the safety of firefighters and the efficient handling of accidents, an intelligent unmanned fire car was designed to simulate fire safety problems in toxic and harmful environments. The functions of this smart car include: automatic driving, image recognition, monitoring of remote and environment, etc., which can help firefighters to extinguish fires in a harmful environment and minimize property damage. © 2024 SPIE.

关键词： Computer vision

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Special Training of Sports Movements Based on machine vision image processing Technology 3rd

Special Training of Sports Movements Based on Machine Vision...

引用

3rd World conference on Intelligent and 3D Technologies, WCI3DT 2024

作者： Yi, Peng Huang, Huamin Wuhan University of Engineering Science Hubei Wuhan430200 China Wuhan Jiaotong School Hubei Wuhan430205 China

ISBN: (纸本)9789819791279

In order to improve the special training effect of sports movements, this paper combines machine vision image coarse-grained technology to build a special training system to improve the special training effect of sports. By building an image acquisition model, the movement area of special action is extracted, and the recognition action is separated from the background, so as to improve the recognition effect. By setting the image threshold, the target recognition action is segmented from the background image, which effectively improves the accuracy of subsequent recognition. Moreover, this paper collects the images of athletes’ special actions, and obtains the structural data and edge features of special actions by integrating action information database, equipment model and other models. In addition, this paper scans the features and matches them with the action identification information, so as to realize the image acquisition of the recognized object. Finally, through the experimental results, we can see that this system has a good effect on the recognition of wrong technical movements, which can effectively improve the special training effect of sports movements. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： machine vision

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Application of Computer vision Technology in the Field of Automatic Target Reporting 14th

Application of Computer Vision Technology in the Field of Au...

引用

14th International conference on Frontier Computing, FC 2024

作者： Zhu, Rui Yu, Jie Yao, Fushan Shi, Wenhua Army Academy of Armored Force Jilin Changchun China The Army of 95795 Guangxi Guilin China

ISBN: (纸本)9789819627936

Computer vision is to measure and judge by machine instead of man, and convert the captured target scene into image signal through camera device. Transmitting it to the image processing system and converting it into a digital signal through the significant mark of the image;Through the calculation of the digital signal, the characteristics of the target are obtained, so as to analyze, sort out, or obtain the judgment results. It is applied to the field of automatic target reporting, so as to improve the efficiency and quality. In view of the problems existing in manual target detection, combined with the actual needs, this paper intends to apply computer vision technology to automatic target reporting, focusing on the basic principle and system composition of automatic target reporting. Combined with the actual application, this paper studies and demonstrates the highly feasible automatic target reporting technology and forms a summary report, Lay a foundation for the subsequent research and development of automatic target reporting system. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： machine vision

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Design of Weld Recognition System Based on machine vision 5

Design of Weld Recognition System Based on Machine Vision

引用

5th International conference on Artificial Intelligence and Advanced Manufacturing, AIAM 2023

作者： Duan, Chenxu Li, Shuangcen School of Mechanical Engineering Sichuan University Jinjiang College Meishan620680 China

ISBN: (纸本)9781839539848

With the rapid progress and innovation of computer vision, image processing and other related disciplines and theoretical methods, machine vision has been more and more widely used. In the process of welding production, there are many kinds of weld defects, such as crack, incomplete penetration, incomplete fusion, porosity and slag inclusion, which affect the quality of parts. The traditional weld defect recognition generally relies on the visual inspection of professional workers, which is inefficient and has a high rate of missed detection, which seriously affects the recognition of defect images. Aiming at this problem, this paper comprehensively discusses the key technologies of weld image processing, weld defect recognition, defect target detection and so on. On this basis, a new weld defect recognition method-target detection method based on image edge detection is proposed, and a weld defect recognition system is designed accordingly. Firstly, the concept and principle of machine vision are introduced, and OpenCV open source vision library is selected as the development platform. Secondly, the basic process of weld defect recognition is analyzed, and the key technologies are studied and analyzed, and compared with the same type of technology. Then, a weld recognition system based on machine vision is designed. Through data analysis and image processing, common weld defects can be automatically identified. Finally, a variety of weld defects were identified to verify the feasibility of the system. © The Institution of Engineering & Technology 2023.

关键词： Computer vision

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ADA-VIT: ATTENTION-GUIDED DATA AUGMENTATION FOR vision TRANSFORMERS 30

ADA-VIT: ATTENTION-GUIDED DATA AUGMENTATION FOR VISION TRANS...

引用

30th IEEE International conference on image processing (ICIP)

作者： Baili, Nada Frigui, Hichem Univ Louisville Comp Sci & Engn Dept 220 Eastern Pkwy Louisville KY 40292 USA

ISBN: (纸本)9781728198354

The limitations of a machine learning model can often be traced back to the existence of under-represented regions in the feature space of the training data. Data augmentation is a common technique that has been used to inflate training datasets with new samples to improve the model performance. However, these techniques usually focus on expanding the data in size and do not necessarily aim to cover the under-represented regions of the feature space. In this paper, we propose an Attention-guided Data Augmentation technique for vision Transformers (ADA-ViT). Our framework exploits the attention mechanism in vision transformers to extract visual concepts related to misclassified samples. The retrieved concepts describe under-represented regions in the training dataset that contributed to the misclassifications. We leverage this information to guide our data augmentation process by identifying new samples and using them to augment the training data. We hypothesize that this focused data augmentation populates under-represented regions and improves the model's accuracy. We evaluate our framework on the CUB dataset and CUB-Families. Our experiments show that ADA-ViT outperforms state-of-the-art data augmentation strategies, and can improve the accuracy of a transformer by an average margin of 2.5% on the CUB dataset and 3.3% on CUB-Families.

关键词： vision Transformer Data Augmentation

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A vision-like network for low-light image enhancement 2

A vision-like network for low-light image enhancement

引用

2nd International conference on image processing, Computer vision and machine Learning, ICICML 2023

作者： Huang, Zishen Li, Jindong Hu, Xiao Ji, Guangyi Guangzhou University School of Mechanical and Electrical Engineering Guangzhou China

ISBN: (纸本)9798350331417

Zero-shot learning is a popular strategy for low-light image enhancement, as it allows convolutional neural networks (CNNs) to be trained without paired data. However, existing zero-shot learning methods often lead to the loss of image details and noise amplification when processing images with extremely low illumination. To address this issue, a vision-like network is proposed, which is inspired by the human visual mechanism of "first the whole, then the part". This lightweight network has only 5349 parameters and is capable of retaining image details. Additionally, a non-reference loss function based on the median filtering algorithm is proposed to reduce information loss caused by rigid denoising. Experimental results show that our method outperforms existing zero-shot learning methods. © 2023 IEEE.

关键词： curve estimation low-light image enhancement noise suppression zero-shot learning

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machine vision and image processing for intelligent online monitoring and control of pharmaceutical fluidized bed granulation processes 4

Machine vision and image processing for intelligent online m...

引用

4th International conference on Computer vision, Application, and Algorithm, CVAA 2024

作者： Liu, Yan Wu, Tao Wang, Xue Zhong Beijing Key Laboratory of Enze Biomass Fine Chemicals College of New Materials and Chemical Engineering Beijing Institute of Petrochemical Technology Beijing102617 China

ISBN: (数字)9781510687622

ISBN: (纸本)9781510687615

Fluidized bed granulation is a unit operation widely used in the pharmaceutical, chemical and food processing industries. It is a manufacturing technology that by suspending lose powders using hot air and transforms the powders into granules of uniform sizes to improve compaction and flow characteristics. The granule size distribution and moisture content are important quality indicators that are currently characterized by sampling and offline analysis in the laboratory, leading to time delay in measurement. This work reports an investigation of machine vision combined with deep learning image segmentation for on-line real-time monitoring. A non-invasive microscopic imaging probe with an integrated light source is designed and mounted on the granulator’s sight glass to monitoring the granule dynamic changes in particle morphology and size. In addition, a near-infrared spectrometer combined with chemometric modeling is used for real-time monitoring of moisture content. © 2025 SPIE.

关键词： Near infrared spectroscopy

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Research on Abnormal Target Recognition of Full Information Mobile Monitoring Based on machine vision 6th

Research on Abnormal Target Recognition of Full Information ...

引用

6th European-Alliance-for-Innovation (EAI) International conference on Advanced Hybrid Information processing (ADHIP)

作者： Wei, Yudong Xia, Yuhong Univ Elect Sci & Technol China Chengdu Coll Chengdu 611731 Peoples R China

ISBN: (纸本)9783031288661;9783031288678

When the color of moving object is close to the background, the accuracy of moving object recognition is affected. So the method of moving object recognition based on machine vision is designed. In order to reduce the distortion of image edge position, the moving object is calibrated and corrected by vision. In order to reduce the influence of noise to a controllable range, the full information mobile monitoring image is enhanced to preserve the image details. The edge features obtained from view and template are calculated by moment, and the similarity is obtained. Then the contour feature of moving monitoring target is extracted based on machine vision. Segmentation of the background region, according to the moving object trajectory center point information such as speed, direction and so on to determine whether the trajectory is abnormal events. The proposed method is tested on INRIA dataset and Vehicle Reld dataset, and the results show that the proposed method can improve the accuracy and recall rate and has good detection performance.

关键词： machine vision Full information Mobile monitoring Abnormal target Target identification Monitoring objectives

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SC-LSTM based Human Action Recognition

SC-LSTM based Human Action Recognition

引用

9th International conference on Signal and image processing (ICSIP)

作者： Kang, Naixin Yi, Pengfei Geng, Qingshu Dong, Jing Liu, Rui Wang, Ling Dalian Univ Sch Software Engn Key Lab Adv Design & Intelligent Comp Minist Educ Dalian Peoples R China

ISBN: (纸本)9798350350920

Human Action Recognition is one of the most applied research directions in the field of Computer vision, which is widely used in human-computer interaction, Augmented Reality (AR) technology, security monitoring, and other scenarios. However, due to the complexity of human action gestures, existing Human Action Recognition methods have certain deficiencies in dealing with variable human gestures and action information, and the accuracy needs to be improved. To improve the accuracy, We propose a multi-dimensional network model based on SC-LSTM(Skip-Connection + LSTM). First, a Temporal Feature Extraction Module is designed based on SC-LSTM, and a Spatial Feature Extraction Module is designed based on CNN and Multi-Attention Mechanism to extract potential human action features from both temporal and spatial dimensions, respectively. Then, a separate SC-LSTM classification network is utilized to process these spatio-temporal features to obtain the final HAR results. The experimental results show that compared to other algorithms, the present model can more fully utilize the information in the temporal dimension, and thus performs better in terms of HAR accuracy.

关键词： Human Action Recognition machine Learning Computer vision Human-Computer Interaction Security

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Embedded machine vision Algorithm Platform Based on Graph Programming 5

Embedded Machine Vision Algorithm Platform Based on Graph Pr...

引用

5th IEEE Global conference for Advancement in Technology, GCAT 2024

作者： Zou, Wangping ChiZhou Vocational And Technical College Anhui Chizhou247100 China

ISBN: (纸本)9798350376685

Simplifying the development method of embedded vision platform can improve the application value of embedded machine vision platform, but there is a problem that the development effect is not satisfactory. Code programming algorithms cannot improve the effect of embedded machine vision platform development, and the development efficiency is low. Therefore, this paper proposes a graph programming algorithm to develop an embedded machine vision platform. First, the image processing data is packaged into graphic components, and developers develop the embedded machine vision platform through the parameter tuning and code generation of the graphic components, and then analyze the comprehensive effect of the developed embedded machine vision platform. The MATLAB simulation results show that the graph programming algorithm proposed in this paper can effectively improve the efficiency of platform development, shorten the platform development cycle, and is superior to the code programming algorithm in all aspects. © 2024 IEEE.

关键词： Development efficiency Flush-in-the-line graph programming algorithm machine vision

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