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检索条件"任意字段=International Conference on Robot Vision and Signal Processing"

共 18150 条记录，以下是541-550 订阅

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A Robust Kalman Filter Based Approach for Indoor robot Positionning with Multi-Path Contaminated UWB Data 48

A Robust Kalman Filter Based Approach for Indoor Robot Posit...

引用

48th IEEE international conference on Acoustics, Speech and signal processing, ICASSP 2023

作者： Cano, Justin Ding, Yi Pages, Gael Chaumette, Eric Le Ny, Jerome Polytechnique Montréal/GERAD Ee Dept. Montréal Canada ISAE-Supaéro Deos France Airbus Sas France

ISBN: (纸本)9781728163277

Ultra-Wide Band (UWB) is a widely used technology to provide real-time and accurate indoor localization to mobile robots, allowing their safe operation in the absence of a satellite-based navigation solution. However, UWB performance suffers from multi-path outliers when signals reflect on surfaces or encounter obstacles. This paper describes an approach to mitigate this issue, based on a M-Estimation Robust Kalman Filter (M-RKF) and leveraging an adaptive empirical variance model for UWB signals. The approach is validated experimentally on a ground robot. © 2023 IEEE.

关键词： Mobile robots

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vision system for automatic recognition of selected road users 25

Vision system for automatic recognition of selected road use...

引用

25th IEEE conference on signal processing - Algorithms, Architectures, Arrangements, and Applications (IEEE SPA)

作者： Balcerek, Julian Konieczka, Adam Pawlowski, Pawel Rusinek, Wojciech Trojanowski, Wojciech Poznan Univ Tech Div Signal Proc & Elect Syst Inst Automat Control & Robot Poznan Poland

ISBN: (数字)9788362065424

ISBN: (纸本)9788362065424

In this article, a system for automatic recognition of selected road users using artificial neural networks is proposed. Five models of neural networks were tested with various configurations. With the special prepared image database, training, validation and tests were conducted to identify five classes of road users: cyclists, people on electric scooters, roller skates, pedestrians and people using personal transport devices. A web application that recognize these classes of road users was also prepared.

关键词： recognition of road traffic participants vision system artificial neural networks mobile application image recognition image processing

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Agri Bot a Smart Agricultural robot for Smart Irrigation and Leaf Detection

Agri Bot a Smart Agricultural Robot for Smart Irrigation and...

引用

2024 IEEE international conference on Intelligent signal processing and Effective Communication Technologies, INSPECT 2024

作者： Das, Archana Cherian, Akash S Prasad, Ashima New Horizon College Of Engineering Department Of ISE Bengaluru India

ISBN: (纸本)9798350379525

This paper presents Agri Bot, a clever automated framework intended to help farmers in overseeing crop well being and water systems. The framework coordinates controller, plant infection discovery, soil dampness detection, and a robotized water siphoning instrument. Agri Bot uses artificial intelligence and ongoing information assortment to improve water use, upgrade crop well being, and diminish work costs. The model was tried on a small farm, bringing about a 30% decrease in water utilization and a 92% precision in early plant sickness identification. The framework's capability to further develop supportability in farming is huge, making it an important resource for small to medium-sized farms. © 2024 IEEE.

关键词： Smart agriculture

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Boosted Spectral Perception via Synergistic vision for Cross-Domain Few-Shot Hyperspectral Image Classification 2

Boosted Spectral Perception via Synergistic Vision for Cross...

引用

2nd IEEE international conference on signal, Information and Data processing, ICSIDP 2024

作者： Yan, Xinyi Li, Jiaojiao Zhang, Zhiyuan Xu, Haitao Zheng, Tie Du, Qian Xidian University Isn Xi'an China Nssc Key Laboratory of Eitss Beijing China Mississippi State University Department of Electrical and Computer Engineering Starkville United States

ISBN: (纸本)9798331515669

Cross-domain few-shot learning (CDFSL) hyper-spectral image classification (HSIC) techniques have significantly enhanced the performance of HSIC under a few annotations. Due to the scattered spatial distribution of HSI and limited labels, the spatial information of the HSI sample is restricted, which means the spectral information may contain more discriminative category features than spatial information. However, most existing CDFSL HSIC methods lack targeted spectral discriminative feature extraction and struggle to capture spectral subtleties. To address this, a Human Eye-like Dual-Branch Network (HEDB-Net) is proposed, comprising a central branch and a peripheral branch. The central branch focuses on extracting core features from complete spectral dimension HSI patches. Simultaneously, these patches are input into the peripheral branch via random sampling and reintegration of spectral bands, enabling the model to perceive initially dispersed spectral information. These two branches represent the model's central and peripheral vision for the spectrum, allowing it to focus on different levels of feature information and significantly boosting spectral perception capabilities. To fully leverage the advantages of synergistic vision, a consistency constraint is applied to align their category probability prediction distributions, which helps ensure consistency in classification decisions between the two branches. Experimental results on the PaviaU dataset demonstrate that HEDBNet outperforms other advanced algorithms in terms of overall accuracy(OA), average accuracy(AA), and Kappa coefficient. This proves that HEDBNet can simultaneously focus on primary and secondary spectral features, thus significantly boosting classification accuracy. © 2024 IEEE.

关键词： Image annotation

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PARAMETER-EFFICIENT vision TRANSFORMER WITH LINEAR ATTENTION 30

PARAMETER-EFFICIENT VISION TRANSFORMER WITH LINEAR ATTENTION

引用

30th IEEE international conference on Image processing (ICIP)

作者： Zhao, Youpeng Tang, Huadong Jiang, Yingying Yong, A. Wu, Qiang Wang, Jun Univ Cent Florida Dept Comp Sci Orlando FL 32816 USA Univ Technol Sydney Sch Elect & Data Engn Sydney NSW Australia Samsung Res China Beijing SRC B Beijing Peoples R China

ISBN: (纸本)9781728198354

Recent advances in vision transformers (ViTs) have achieved outstanding performance in visual recognition tasks, including image classification and detection. ViTs can learn global representations with their self-attention mechanism, but they are usually heavy-weight and unsuitable for resource-constrained devices. In this paper, we propose a novel linear feature attention (LFA) module to reduce computation costs for vision transformers and combine efficient mobile CNN modules to form a parameter-efficient and high-performance CNN-ViT hybrid model, called LightFormer, which can serve as a general-purpose backbone to learn both global and local representation. Comprehensive experiments demonstrate that LightFormer achieves competitive performance across different visual recognition tasks. On the ImageNet-1K dataset, LightFormer achieves top-1 accuracy of 78.5% with 5.5 million parameters. Our model also performs well when transferred to object detection and semantic segmentation tasks. On the MS COCO dataset, LightFormer attains mAP of 33.2 within the YOLOv3 framework, and on the Cityscapes dataset, with only a simple all-MLP decoder, LightFormer achieves mIoU of 78.5 and FPS of 15.3, surpassing state-of-the-art lightweight segmentation networks.

关键词： vision transformers self-attention image classification semantic segmentation

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JOINT EGO-NOISE SUPPRESSION AND KEYWORD SPOTTING ON SWEEPING robotS 47

JOINT EGO-NOISE SUPPRESSION AND KEYWORD SPOTTING ON SWEEPING...

引用

47th IEEE international conference on Acoustics, Speech and signal processing (ICASSP)

作者： Na, Yueyue Wang, Ziteng Wang, Liang Fu, Qiang Alibaba Grp Hangzhou Peoples R China Sun Yat Sen Univ SYSU Sch Elect & Commun Engn Guangzhou 510275 Guangdong Peoples R China

ISBN: (纸本)9781665405409

Keyword spotting is necessary for triggering human-machine speech interaction. It is a challenging task especially in low signal-to-noise ratio and moving scenarios, such as on a sweeping robot with strong ego-noise. This paper proposes a novel approach for joint ego-noise suppression and keyword detection. The keyword detection model accepts outputs from multi-look adaptive beamformers. The noise covariance matrix in the beamformer is in turn updated using the keyword absence probability given by the model, forming an end-to-end loop-back. The keyword model also adopts a multi-channel feature fusion using self-attention, and a hidden Markov model for online decoding. The performance of the proposed approach is verified on real-word datasets recorded on a sweeping robot.

关键词： sweeping robot ego-noise beamforming speech enhancement keyword spotting

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LSDM-PCB: A LIGHTWEIGHT SMALL DEFECT DETECTION MODEL FOR PRINTED CIRCUIT BOARD 31

LSDM-PCB: A LIGHTWEIGHT SMALL DEFECT DETECTION MODEL FOR PRI...

引用

2024 international conference on Image processing

作者： Zeng, Qi Zhao, Chongren He, Pengfei Gao, Hongchao Dongguan Univ Technol Dongguan Peoples R China OPT Machine Vision Tech Co Ltd Dongguan Peoples R China

ISBN: (纸本)9798350349405;9798350349399

The defects of the Printed Circuit Board(PCB) directly affect the performance and reliability of electronic products. Therefore, detecting PCB defects is crucial. Lightweight models in PCB production inspection can effectively reduce equipment costs, but they exhibit limited feature extraction capabilities. Moreover, complex background conditions can interfere with the model's ability to locate and recognize small defects. To address these challenges, we propose LSDM-PCB, a lightweight PCB defect detection model based on YOLOv8n. Firstly, we improve the network structure to reduce the number of model parameters while enhancing the model's ability to capture small defects. Additionally, we adopt Receptive-Field Attention Convolution(RFAConv) as a downsampling module to enhance the model's feature extraction by considering the importance of each feature within the receptive field. Finally, we propose a Global and Local Mixed Attention(GLMA) mechanism to strengthen multi-scale feature representation, allowing the model to focus more on small defects. Results show LSDM-PCB reduces model parameters by 74% and improves mAP50 to 96.8%, a 2.7% enhancement compared to the baseline model YOLOv8n.

关键词： PCB defect detection RFAConv GLMA mechanism

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Human-machine Integration of Lower Limb Rehabilitation robot

Human-machine Integration of Lower Limb Rehabilitation Robot

引用

2024 international conference on Advanced Mechatronic Systems, ICAMechS 2024

作者： Zhang, Xiang Wang, Aihui Gao, Shengda Duan, Huichao Li, Hengyi Yue, Xuebing Zhongyuan University of Technology School of Automation and Electrical Engineering Zhengzhou450007 China

ISBN: (纸本)9798350366204

As the prevalence of lower limb motor dysfunction due to conditions such as cerebral palsy, hemiplegia, and paraplegia continues to rise, the current medical infrastructure for sports rehabilitation is insufficient to meet the growing demand. There is also a serious shortage of rehabilitation doctors. In this context, lower limb rehabilitation robots have emerged as a critical role in the rehabilitation process. These robots not only alleviate the workload of rehabilitation doctor but also accelerate patient recovery. Aiming at the shortage in rehabilitation medical resources, this paper introduces a self-developed lower limb rehabilitation robot. This paper encompasses the comprehensive architecture design, hardware development, data collection and processing, control signal transmission, among other aspects. Experimental results demonstrate that the self-developed robot can effectively delivers gait trajectories tailored to the needs of sports rehabilitation, thereby enhancing the efficacy of rehabilitation programs. © 2024 IEEE.

关键词： robots

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vision system for automatic recognition of Polish historic vehicles 25

Vision system for automatic recognition of Polish historic v...

引用

25th IEEE conference on signal processing - Algorithms, Architectures, Arrangements, and Applications (IEEE SPA)

作者： Balcerek, Julian Dabrowski, Adam Pawlowski, Pawel Rusyniak, Jedrzej Poznan Univ Tech Div Signal Proc & Elect Syst Inst Automat Control & Robot Poznan Poland

ISBN: (数字)9788362065424

ISBN: (纸本)9788362065424

In this article, a system for automatic recognition of Polish historic vehicles is proposed. A unique database of vehicle images was prepared, including real and scale vehicles. A software was developed to automatically classify vehicle models and assess their condition using artificial neural networks. Training, validation and test were performed for various datasets and neural network models. In the best case the VGG16 network reached 95% of vehicle model classification accuracy. In addition, a web application visualizing vehicle recognition is offered.

关键词： Polish historic vehicles vision system artificial neural networks web application image recognition image processing

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Comparative Performance Analysis of PI Controller and MRAC for a Differential Drive robot 5

Comparative Performance Analysis of PI Controller and MRAC f...

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5th international conference on Communications, signal processing, and their Applications (ICCSPA)

作者： Khalil, Ahmed Mukhopadhyay, Shayok Rehman, Habibur Amer Univ Sharjah Dept Elect Engn Sharjah U Arab Emirates

ISBN: (纸本)9781665482370

This paper compares the performance of two control techniques on a DC motor-driven differential drive robot. Regular cascaded PI controller is the first scheme. In the second scheme, the outer PI controller is replaced by direct model reference adaptive control (MRAC). The main objective of the motor controller is that it follows a desired angular speed. Both schemes are compared in terms of average absolute speed error and power consumption. Preliminary simulations are performed on a DC motor model;whereas, experimental runs are done on a differentially driven robot powered by DC motors, to compare the performance of both control laws. It is found that the control scheme with MRAC has lesser speed error and power consumption compared to the PI controller, both in simulation and the experimental investigation.

关键词： DC motor Model reference adaptive control (MRAC)

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