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7th International Conference on Wireless Communications, Networking and applications, WCNA 2023

作者： Lu, Yufan Zhejiang Gongshang University Hangzhou China

ISBN: (纸本)9789819624089

This research aims to improve the visual target detection and recognition capabilities of shopping robots in various sales environments by optimizing and improving the YOLO algorithm, in order to improve accuracy and real-time performance. The research method involves embedded spatial hierarchical sampling technology and it adapts to image processing of different sizes, uses a separate convolutional neural network structure to reduce computational complexity, and cultivates a more concise network model by refining the effective data of complex models. Experimental results show that the improved YOLO algorithm performs well in weak Its average accuracy has been significantly improved under light, medium light and strong light environments, especially in the detection of small items. A study shows that improved programming significantly improved the vision of shopping assistance robots. Recognition capabilities enable robots to provide more accurate and faster services in real shopping environments. © The Author(s) 2025.

关键词： machine vision

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machine vision enabled characterization of defects and their fatigue effects in additively manufactured steels

Machine vision enabled characterization of defects and their...

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applications of machine Learning 2024

作者： Cotrina, J. Amorin Uysalel, C. Olumor, I. Torresani, E. Olevsky, E. Ghazinejad, M. Dept. of Mechanical and Aerospace Engineering UC San Diego La Jolla CA92093 United States Dept. of Mechanical Engineering San Diego State University San DiegoCA92182 United States

ISBN: (纸本)9781510679368

We characterized manufacturing-induced defects in 316L stainless steels - fabricated by direct metal laser sintering (DMLS) - and investigated their roles in the fatigue behavior of steel parts. The primary defects targeted are porosities, inner cracks, and edge cracks. We used Convolutional Neural Networks (CNNs) to detect and classify these defects and moved toward a machine vision-based metrology technique for metal additive manufacturing (AM). The Moore cyclic loading method was applied to characterize the fatigue behavior of 316L samples. The results indicate a strong correlation between the quality of additive manufacturing, defect levels, and the fatigue properties of the steel samples. Specifically, samples with lower defect levels exhibited significantly higher load endurance and longer life cycles. To further explore the influence of defects on mechanical behavior, we applied image processing techniques to measure the density, size, morphology, and location of defects in the steels. The quantification of AM defects features paves the way for a deeper understanding of microstructure - macro-behavior relations and enhanced fatigue prediction models in additively manufactured steels. © 2024 SPIE.

关键词： Laser heating

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Design and Development of Industrial vision Sensor (IVIS) for Next Generation Industrial applications 19

Design and Development of Industrial Vision Sensor (IVIS) fo...

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19th IEEE-India-Council International Conference (INDICON)

作者： Daniel, Jerry J. Thomas, Lijo Lajitha, C. S. Mathew, Jacob T. Jithin, S. Mohan, Anju Kumar, Kichu S. C DAC Control & Instrumentat Grp Thiruvananthapuram Kerala India

ISBN: (纸本)9781665473507

Industrial automation is undergoing a tremendous change due to the proliferation of the concepts, the Internet of Things (IoT), Cyber-Physical Systems (CPS) and tactile internet, which enables the interconnections of factory floor devices and enterprise network on a wider and fine-grained scale. vision Sensor deployments are getting great momentum in factories, as it improves the quality and productivity of the systems being inspected. Smart vision Sensors[1] removes the need of the additional infrastructures for running the image processing algorithms and vision applications, by directly running the vision logic on the device and control/monitor the various parameters on the field based on the image processing outputs. Industrial vision sensor (IVIS) is an industrial smart camera, which has a CMOS image sensor[2] and a powerful on-board processing system capable of supporting machine vision applications, for improving the product and process qualities and thereby improve the yield and profit. IVIS is capable of extracting applicationspecific information from the captured images and make decisions based on the image processing algorithms implemented on the system, to realize stand-alone intelligent and decision-making automation system. In this paper we present the design and development of IVIS, its application domains and preliminary test results.

关键词： CPS IVIS CMOS

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Intelligent Optimization of Computer image processing Technology Analysis 3rd

Intelligent Optimization of Computer Image Processing Techno...

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3rd EAI International Conference on Application of Big Data, Blockchain, and Internet of Things for Education Informatization, BigIoT-EDU 2023

作者： Wei, Huayong Anhui Communications Vocational and Technical College Hefei China

ISBN: (纸本)9783031631382

Intelligent optimization algorithm is an advanced computing technology, which simulates the biological evolution process in nature or the logical thinking of human beings to find a solution to the problem. In computer image processing, intelligent optimization algorithms are widely used, mainly in image enhancement, image restoration, image segmentation, feature extraction, image recognition and so on. Intelligent optimization algorithms have developed rapidly, and many excellent algorithms with different characteristics have emerged, which have achieved good results in practical applications. image analysis is the basis for realizing machine vision, including image enhancement, image fusion, image recognition, image tracking, image retrieval and many other technologies. It has a great demand in medicine, transportation, military, aerospace and other fields. In particular, the development of many industries and fields such as intelligent robots, smart medicine, and smart cities has brought many optimization challenges to image analysis, At present, image analysis based on swarm intelligence optimization algorithm has become an important research hotspot. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2024.

关键词： image enhancement

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Facial micro-expression recognition based on dual-stream fusion network

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Multimedia Tools and applications 2025年 1-14页

作者： Sun, Jiacheng Chen, Changhong Jiangsu Key Laboratory of Intelligent Information Processing and Communication Technology Nanjing University of Posts and Telecommunications No.66 Xin Mofan RD Nanjing210003 China

Micro-expressions(MEs) have emerged as a viable strategy for affective estimation due to their high reliability in emotion detection. In recent years, deep learning methods have been successfully applied to the field of micro-expression recognition. However, extracting and learning features from MEs presents challenges due to their brief duration and subtle intensity. To address these challenges, we propose the dual-stream fusion network (DSFNet). Specifically, we design shallow tokens-to-token vision transformers (T2T-ViT) to effectively capture comprehensive spatial position information. We also fine-tuned the number of ViT encoders and heads to enhance overall model performance. Additionally, the proposed multiscale convolution block (MCB) and attention mechanism modules (AMM) facilitate the effective extraction of detailed and valuable multiscale features from MEs. By employing various sizes of convolutional kernels and attention mechanisms, our approach captures higher-level image information, thereby improving MER accuracy. Finally, we integrate the information obtained from both branches. Performance evaluations on three mainstream ME datasets-SMIC, CASME ii, and SAMM-demonstrate that the proposed framework significantly outperforms other advanced methods in micro-expression classification. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Deep learning

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Prediction of Handwritten Classification using CNN Techniques 9

Prediction of Handwritten Classification using CNN Technique...

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9th International Conference on Signal processing and Communication, ICSC 2023

作者： Sanu, Kumar Bhandari, Rahul Computer Science and Engineering Chandigarh University Chandigarh India

ISBN: (纸本)9798350383201

Today's computer vision industry makes extensive use of image recognition. A popular method of image recognition is digit recognition. The recognition of handwritten numbers is one of the most well-known difficulties in computer vision and machine learning applications. In essence, this model proposes an online approach for recognizing handwritten digits (HDR) by utilizing convolutional neural networks (CNN).As a training sample, the technique uses the MINST dataset, which contains centered 28x28 gray scale images of handwritten numbers. It has 10,000 test cases and 60,000 training examples. This paper shows the accuracy rate and loss of the model. Lastly, execute the trials using a variety of random handwritten 28x28 pixel digits. © 2023 IEEE.

关键词： Computer vision

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Self-powered photodetector based on 1D TiO2-3D CdS mixed dimensional heterostructure fabricated at low temperature

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SOLAR ENERGY 2024年 274卷

作者： Reddy, B. Kumaar Swamy Veerappan, Ganapathy Badhulika, Sushmee Borse, Pramod H. Ctr Solar Energy Mat Int Adv Res Ctr Powder Met & New Mat ARCI Hyderabad 500005 India Indian Inst Technol Hyderabad IITH Dept Elect Engn Hyderabad Telangana India

Solution processed photodetectors have garnered great attention in applications such as, machine vision perception, neuromorphic computing and opto-electronic memory storage. Though, such photodetectors offer several advantages such as ease of fabrication, high scalability, low thermal budget and low-cost processing, multi-modal functionality etc. however, they suffer from the major drawback of inferior device performance -as low responsivity and slow rise time, particularly due to the intrinsic poor crystallinity of the photoactive material. In this work, we demonstrate a solution processed photodetector with impressive performance at comparatively low processing temperatures (<150 degrees C) based on the mixed dimensional heterostructure configuration of 1D TiO2 nanorods and 3D CdS nanoflowers. TiO2 nanorods have been synthesized by hydrothermal technique, whereas their CdS sensitization is done by chemical bath deposition. Low cost carbon paste is used as electrode instead of conventional non-economic noble metal electrodes. X-ray diffraction studies validated excellent crystallinity of the photoactive material even under low temperature processing condition. The type-ii Heterojunction (TiO2 and CdS) configuration photodetector shows efficient response at zero bias, thus yielding a self-powered device. The detector shows response in UV and visible region, with excellent responsivity of 110 mA/W (5 V), 563 A/W (0 V) and a quicker rise time of 81 ms. Albeit the simple fabrication scheme and low processing temperatures, the detector exhibited promising figures-of-merit, which aids in fabrication of novel solution processed photodetectors.

关键词： TiO 2 nanorods Low-temperature processed Solution processing Self-powered detector Broadband photodetector

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A Survey on Attention Mechanisms for Medical applications: are we Moving Toward Better Algorithms?

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IEEE ACCESS 2022年 10卷 98909-98935页

作者： Goncalves, Tiago Rio-Torto, Isabel Teixeira, Luis F. Cardoso, Jaime S. Univ Porto Inst Syst & Comp Engn Technol & Sci P-4200465 Porto Portugal

The increasing popularity of attention mechanisms in deep learning algorithms for computer vision and natural language processing made these models attractive to other research domains. In healthcare, there is a strong need for tools that may improve the routines of the clinicians and the patients. Naturally, the use of attention-based algorithms for medical applications occurred smoothly. However, being healthcare a domain that depends on high-stake decisions, the scientific community must ponder if these high-performing algorithms fit the needs of medical applications. With this motto, this paper extensively reviews the use of attention mechanisms in machine learning methods (including Transformers) for several medical applications based on the types of tasks that may integrate several works pipelines of the medical domain. This work distinguishes itself from its predecessors by proposing a critical analysis of the claims and potentialities of attention mechanisms presented in the literature through an experimental case study on medical image classification with three different use cases. These experiments focus on the integrating process of attention mechanisms into established deep learning architectures, the analysis of their predictive power, and a visual assessment of their saliency maps generated by post-hoc explanation methods. This paper concludes with a critical analysis of the claims and potentialities presented in the literature about attention mechanisms and proposes future research lines in medical applications that may benefit from these frameworks.

关键词： Biomedical imaging Computer architecture Transformers Medical services Deep learning Artificial intelligence Biomedical equipment Computer vision Artificial intelligence attention mechanisms computer vision deep learning medical applications medical image analysis transformers

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Visual information perception system of coal mine comprehensive excavation working face for edge computing terminal

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IET image processing 2024年第12期18卷 3681-3698页

作者： Zhao, Dongyang Su, Guoyong Wang, Pengyu Anhui Univ Sci & Technol State Key Lab Min Response & Disaster Prevent & Co 168 Taifeng St Huainan Anhui Peoples R China Anhui Univ Sci & Technol Sch Mech & Elect Engn Huainan Peoples R China

Aiming at the problems of low detection accuracy, high computational complexity and long-time consumption of visual perception model in a complex mining environment, this research designs a visual information perception system of coal mine comprehensive excavation working face for an edge computing terminal. Firstly, the C3-Fast feature extraction module, spatial pyramid pooling with cross-stage partial connection (SPPCSPC) pooling module, bi-directional feature pyramid network and lightweight decoupled detection head are used to optimize the YOLOv5s model, so as to construct the FSBD-YOLOv5s multi-object detection model. Secondly, the pruning and distillation algorithm is used to lighten the FSBD-YOLOv5s model, and the model complexity is greatly reduced while maintaining the model detection accuracy. Further, the lightweight FSBD-YOLOv5s model is migrated and deployed to the edge computing terminal platform and the TensorRT engine is used to accelerate model inference. Finally, experiments are carried out based on the data set of the coal mine comprehensive excavation working face. The experimental results show that on the edge computing terminal platform, the parameters and computational volume of the lightweight FSBD-YOLOv5s model are reduced by 50.8% and 34.0%, while its detection accuracy and speed reach 94.0% and 43.7 fps, which can fully satisfy the requirements of the accuracy and real-time for the coal mine engineering applications. In the complex operation scene of coal mine, due to adverse environmental factors such as uneven illumination, high dust and mixed man-machine multi-target, the speed and measurement accuracy of traditional visual perception model decrease sharply. In order to solve the above problems, this study proposes to build a visual information perception system for coal mine comprehensive excavation working face for edge computing terminal and combines channel pruning algorithm, knowledge extraction algorithm and TensorRT acceleration e

关键词： computer vision convolutional neural nets embedded systems feature extraction image recognition object detection visual perception

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Sensors, systems and algorithms of 3D reconstruction for smart agriculture and precision farming: A review

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COMPUTERS AND ELECTRONICS IN AGRICULTURE 2024年 224卷

作者： Yu, Shuwan Liu, Xiaoang Tan, Qianqiu Wang, Zitong Zhang, Baohua Nanjing Agr Univ Coll Artificial Intelligence Nanjing Jiangsu Peoples R China Southeast Univ Coll Automat Nanjing Jiangsu Peoples R China

Perceiving the shape and structure of the real three-dimensional world through sensors and cameras is indispensable across various domains. The 3D reconstruction technology is dedicated to realizing this ideal process. 3D reconstruction technology serves as a transformative tool, enriching our ability to perceive the genuine shape and stereo structure of objects and scenes in the real world. Through combining advanced sensors, image processing algorithms and 3D reconstruction methods, it captures the shape and structural information of targets from multiple perspectives and dimensions, and creates highly realistic 3D models in the virtual environment. With the rapid modernization of agriculture and ongoing technological progress, the demand for more efficient and precise management and monitoring methods in agricultural production is increasing. Traditional observation and measurement methods face challenges such as low efficiency and incomplete data. 3D reconstruction technology provides more accurate and intelligent management tools for smart agriculture. This paper provides a detailed introduction to the research progress based on 3D reconstruction technology in smart agriculture. It delves into the characteristics and development of various sensors and sensing systems, discussing various methods to implement 3D reconstruction technology. Different from applications in industrial environments, agricultural environments and crops are usually complex and variable, and consideration of diverse factors is required for the selection of suitable sensors and reconstruction methods. Therefore, several aspects of applications are summarized, such as agricultural robotics, crop phenotyping, livestock, and the food industry. Finally, the challenges and potential future trends of 3D reconstruction in agriculture are given.

关键词： 3D reconstruction Smart agriculture Precision farming machine vision Agricultural robotics Crop phenotyping

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