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

共 18111 条记录，以下是421-430 订阅

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Research on Resource Allocation Strategy of Swarm robot Based on Historical Information 6

Research on Resource Allocation Strategy of Swarm Robot Base...

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6th international conference on Sensors, signal and Image processing (SSIP)

作者： Sun, Yi Yang, Zepeng Xian Univ Sci & Technol Coll Commun & Informat Engn Xian 710054 Peoples R China

ISBN: (纸本)9798400707995

To solve the communication resource allocation problem of swarm robots in emergency rescue scenarios, an evolutionary game-based resource allocation strategy is proposed. The complex dynamic equation of the evolutionary game is constructed through the payoff function, cost function and utility function of the resource consumption robot. The monotonicity of the replication dynamic equation is proved and the evolutionary game equilibrium solution is obtained. For the time-varying characteristics of emergency scenarios, the evolutionary game model is optimized by introducing historical information conditions within the memory length in the evolution of replication dynamic equations. By simulation, the proposed evolutionary game model is evaluated. The results show that the distribution of the population tends to converge and exists stably after 25 iterations. By comparing the historical average income information with the current income information under different memory lengths, it is found that the convergence speed is the fastest and the convergence is the best when the medium memory length m=5.

关键词： Communication resource allocation replication dynamic equation swarm robot historical information time-variant channel

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MULTI-TASK MODEL BASED ON vision TASK LEVEL FOR SALIENCY OBJECT DETECTION IN FOGGY CONDITIONS 30

MULTI-TASK MODEL BASED ON VISION TASK LEVEL FOR SALIENCY OBJ...

引用

30th IEEE international conference on Image processing (ICIP)

作者： Zhu, Yusen Zhou, Gang Ren, Jingxu Tian, Jiakun Jia, Zhenhong Xinjiang Univ Key Lab Signal Detect & Proc Urumqi 830046 Peoples R China

ISBN: (纸本)9781728198354

In recent years, saliency object detection methods based on convolutional neural networks have been widely studied,and have achieved excellent performance in clear images. However, due to the low visibility of images in foggy conditions, the existing saliency object detection methods will be seriously affected or even ineffective. To address this problem, we introduce an end-to-end multi-task learning network. We design two subetworks for depth estimation and image restoration as auxiliary tasks to improve saliency object detection in foggy conditions. According to different characteristics of vision tasks, different shared layers are assigned to improve the performance of saliency object detection. Experiments show that our method has been greatly improved on both synthetic foggy datasets and real-to-world foggy datasets, outperforming many state-to-the-art saliency object detection methods.

关键词： saliency object detection multi-task learning foggy conditions

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GROUP MASKED MODEL LEARNING FOR GENERAL AUDIO REPRESENTATION 30

GROUP MASKED MODEL LEARNING FOR GENERAL AUDIO REPRESENTATION

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30th IEEE international conference on Image processing (ICIP)

作者： Atito, Sara Awais, Muhammed Alex, Tony Kittler, Josef Univ Surrey Ctr Vis Speech & Signal Proc CVSSP Guildford Surrey England Univ Surrey Surrey Inst People Ctr AI Guildford GU2 7XH Surrey England

ISBN: (纸本)9781728198354

vision transformers have recently generated significant interest in the computer vision and audio communities due to their flexibility in learning long-range relationships. However, transformers are known to be data hungry which require orders of magnitude more data [1] to train. This has motivated the research in self-supervised pretraining of audio transformers, which reduces the dependency on large amounts of labeled data and focuses on extracting concise representation of the audio spectrograms. In this paper, we propose Audio-GMML, a self-supervised transformer for general audio representations that is based on Group Masked Model Learning (GMML) and a patch aggregation strategy to improve the performance of learned representations and enforce global structure of the given audio. We evaluate our pretrained models on several downstream tasks, setting a new state-of-the-art performance on five audio and speech classification tasks. The code and pretrained weights will be made publicly available for the scientific community.

关键词： Audio Spectrograms Self-supervised Learning vision Transformers GMML

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2025 4th international conference on robotics, Electrical and signal processing Techniques, ICREST 2025

2025 4th International Conference on Robotics, Electrical an...

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4th international conference on robotics, Electrical and signal processing Techniques, ICREST 2025

ISBN: (纸本)9798331530938

The proceedings contain 96 papers. The topics discussed include: optimizing day-ahead charging schedules for electric vehicles: a multi-factor priority-based approach;enhancing road navigation for the visually impaired: a laser-based night vision approach;hybrid energy-based battery storage swapping station for electrical vehicles and net metering;a correlative abstraction between the scenario of no-retransmission and retransmission process by utilizing evolutionary game theory for self-organized data aggregation mechanism;designing and manufacturing of Robo nurse with fully articulating hand;impact of battery operated three wheeler on local grid system;improved breast cancer detection in ultrasound images using masked image integration and transfer learning;and production cost and emissions minimization based on renewable energy using superiority of feasible solutions- moth flame optimization.

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Principal Curvatures as Pose-Invariant Features of Depth Maps for RGB-D Object Recognition 13

Principal Curvatures as Pose-Invariant Features of Depth Map...

引用

13th international conference on Image processing Theory Tools and Applications

作者： Morisset, Maxime Donias, Marc Germain, Christian Univ Bordeaux Lab IMS Signal & Image Proc Grp Talence France

ISBN: (纸本)9798331541859;9798331541842

Computer vision tasks, such as object recognition, using deep learning find their place in a variety of contexts including agriculture. Regarding data, the coupling of RGB and depth modalities has already proven to be beneficial for object recognition over the use of RGB-only images. However, the lack of neural network architectures and large-size datasets dedicated to the depth modality forces us to use backbones pre-trained on RGB data using large datasets such as ImageNet. While works proposed by Eitel et al. and Aakerberg et al. rely on colorizing the depth values to match an RGB format, they do not take full advantage of the geometric properties carried by the depth modality. We demonstrated principal curvatures when used to color-encode the depth values retain more information related to the object's shape. The proposition was evaluated on the Washington RGB-D dataset and gave mitigated results mainly explained by a high confusion between similarly shaped objects, which represent an important fraction of the dataset. With the introduction of superclasses based on the geometric shape of objects (sphere, cylinder, cube,...) our model performed higher than the previous work, e.g. 3.1% precision increase for the sphere superclass. While presenting some limitations, this work opens the path for further developments.

关键词： RGB-D Transfer Learning Principal Curvatures Pose Invariance Computer vision Convolutional Neural Networks Deep Learning Artificial Intelligence

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ROBUST FACE ANTI-SPOOFING FRAMEWORK WITH CONVOLUTIONAL vision TRANSFORMER 30

ROBUST FACE ANTI-SPOOFING FRAMEWORK WITH CONVOLUTIONAL VISIO...

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30th IEEE international conference on Image processing (ICIP)

作者： Lee, Yunseung Kwak, Youngjun Shin, Jinho KakaoBank Corp Div Res & Dev Seongnam South Korea Korea Adv Inst Sci & Technol Dept Elect Engn Seoul South Korea

ISBN: (纸本)9781728198354

Owing to the advances in image processing technology and large-scale datasets, companies have implemented facial authentication processes, thereby stimulating increased focus on face anti-spoofing (FAS) against realistic presentation attacks. Recently, various attempts have been made to improve face recognition performance using both global and local learning on face images;however, to the best of our knowledge, this is the first study to investigate whether the robustness of FAS against domain shifts is improved by considering global information and local cues in face images captured using self-attention and convolutional layers. This study proposes a convolutional vision transformer-based framework that achieves robust performance for various unseen domain data. Our model resulted in 7.3%p and 12.9%p increases in FAS performance compared to models using only a convolutional neural network or vision transformer, respectively. It also shows the highest average rank in sub-protocols of cross-dataset setting over the other nine benchmark models for domain generalization.

关键词： Face Anti-Spoofing Domain Generalization Convolutional vision Transformer Global and Local Features Hybrid Feature Extraction

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A Two-Stage Monocular vision Detection Method for 6D Pose Estimation in Multi-Heterogeneous robot Systems

A Two-Stage Monocular Vision Detection Method for 6D Pose Es...

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2024 international conference on Intelligent Systems and robotics, CISR 2024

作者： Shan, Bo Zhao, Donghui Yang, Junyou Liu, Houde Shenyang University of Technology Shenyang110870 China Jianghuai Advance Technology Center Hefei230000 China Tsinghua University Shenzhen518005 China

Multi-robot systems are crucial for enhancing work efficiency, flexibility, and adaptability, with the accurate acquisition and processing of pose information in real-world environments being a prerequisite for their implementation. This study proposes a 6D pose visual localization system for heterogeneous multi-robot systems based on monocular vision, addressing the issues of high costs and suboptimal real-time performance in existing systems. The main contributions include the design of a cost-effective and efficient monocular vision localization system framework, the proposal of an enhanced YOLOv8 detection model that integrates object classification, keypoint detection, and 6D pose analysis, and the development of a 6D pose analysis method based on 3D model-based PnP keypoint threshold screening and matching, thereby improving the system's operational speed and accuracy. This research achievement can be applied in multiple fields such as multi-heterogeneous robot system coordination verification, autonomous navigation, and intelligent manufacturing. © Published under licence by IOP Publishing Ltd.

关键词： Multipurpose robots

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Design of Automatic Identification Algorithm for Double-feature Fault signal Waveform of Power Equipment

Design of Automatic Identification Algorithm for Double-feat...

引用

2023 international conference on Algorithm, Imaging processing, and Machine vision, AIPMV 2023

作者： Tang, Huidong Li, Duo Lei, Wendong Meng, Jinpeng Shenzhen Power Supply Bureau Co. Ltd Guangdong China

ISBN: (纸本)9781510672444

The conventional automatic identification algorithm of double-feature fault signal waveform of power equipment mainly uses ART (Adaptive Resonnance Theory) network for classification and discrimination, which is easily influenced by the identification mapping relationship, resulting in low correct identification rate of fault signal waveform. Therefore, it is necessary to design a brand-new automatic identification algorithm of double-feature fault signal waveform of power equipment. That is to say, the waveform characteristics of dual-feature fault signal of power equipment are extracted, and the optimization algorithm for automatic identification of dual-feature fault signal waveform of power equipment is generated, so that the automatic identification of fault signal waveform is realized. The experimental results show that the designed double-feature fault signal waveform automatic identification algorithm for power equipment has a high correct fault identification rate, which proves that the designed double-feature fault signal waveform automatic identification algorithm for power equipment has good identification effect, reliability and certain application value, and has made certain contributions to improving the operation safety of power equipment. © 2024 SPIE.

关键词： Terminals (electric)

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Research and Application of Small Target Detection Algorithm Based on Bipedal robot 6

Research and Application of Small Target Detection Algorithm...

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6th IEEE international conference on Automation, Electronics and Electrical Engineering, AUTEEE 2023

作者： Kong, Weigang Qu, Zenghui Liu, Haiying Deng, Lixia Qilu University of Technology Shandong Academy of Sciences School of Information and Automation Jinan250353 China

ISBN: (纸本)9798350305623

With the rapid development and popularisation of robotics, bipedal robotics is widely used in various fields. The robot platform used in this paper is the NAO bipedal robot, focusing on the small target detection algorithms and applications of bipedal robots. Due to the limitations of the hardware of the bipedal robot, there are certain noise interference and blurring of the picture quality in the target detection, especially in the case of unstable external light intensity. In this paper, three target detection algorithms are compared and experimented to select the algorithm with excellent detection effect. And through the process of image vision pre-processing of the collected images further processing, analysis of the three colour space of the collected images, selected the least affected by the light of the YUV colour space as the NAO bipedal robot target detection of the colour space, and then the implementation of the image segmentation, image noise reduction process improves the ability of bipedal robot target detection. © 2023 IEEE.

关键词： Image processing NAO robot Target detection

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Design and Optimization of a Cable Tension Force Sensor for a Low-cost Custom Continuum robot Actuator

Design and Optimization of a Cable Tension Force Sensor for ...

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IEEE/ASME international conference on Advanced Intelligent Mechatronics (AIM)

作者： Nguyen, Dai-Dong Nguyen, Phuc Thanh-Thien Su, Shun-Feng Kuo, Yu-Cheng Kuo, Chung-Hsien Natl Taiwan Univ Sci & Technol Dept Elect Engn Taipei 106 Taiwan Natl Taiwan Univ Dept Mech Engn Taipei 106 Taiwan

ISBN: (纸本)9798350355376;9798350355369

In recent years, cable-driven continuum robots have emerged as a notable focus in robotic research. Achieving a stable and precise controller for such robots requires careful consideration of measuring cable tension force. This paper introduces an innovative design and optimization approach using strain gauges to create a cable tension force sensor (CTFS) that is stable, low-noise, and cost-effective. The methodology involves finite element method (FEM) analysis and optimization to determine the optimal sensor structure based on design requirements. The CTFS design structure is fabricated with 3 types of strain gauges arrangement to conduct the generality. signal processing circuits for the sensor are developed to handle signals from the CTFS strain gauge bridge. Additionally, the study details the fabrication of three types of CTFS modules to comprehensively assess the proposed designs. Experimental validations, including calibration and temperature sensitivity, are conducted to verify the CTFS in various aspects. The experimental results of the cable tension force sensor shows that the CTFSs can provide high sensitivity with 0.31mV/N, 0.37586mV/N and 0.3197mV/N along with high stability for CTFS-Type1, CTFS-Type2 and CTFS-Type3 respectively. Finally, the application validation demonstrates the potential effectiveness of the cable tension force sensor for affordable, custom continuum robot actuators.

关键词： Tension force sensor structural topology design and optimization force signal processing cable-driven continuum robots

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