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Based on the improved YOLOV8 small target detection algorithm

Based on the improved YOLOV8 small target detection algorith...

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2024 international conference on Mechatronic Engineering and Artificial Intelligence, MEAI 2024

作者： Jia, Libin Gao, Hongwei Wang, Qingyv Jiao, Canguang Sun, Jian School of Automation and Electrical Engineering Shenyang Ligong University Liaoning Shenyang110159 China Institute of International Engineering Shenyang Ligong University Liaoning Shenyang110159 China China State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Liaoning Shenyang110159 China

ISBN: (纸本)9781510689176

With the rapid development of drone technology, drone-based target detection has become a hot topic in current research. However, existing methods face challenges such as low detection accuracy and poor real-time performance. To address this issue, we propose an improved target detection method named YOLOV8-SNP for application in aerial images. The algorithm combines a more lightweight startnet backbone network to enhance detection speed. Additionally, we utilize the consistent dual assignment strategy(CDAS)method to accelerate post-processing and add a dedicated layer for small targets to improve their accurate detection. Experimental results show that our YOLOV8-SNP improves detection accuracy by 3% compared to YOLOV8 on the Visdrone dataset. © 2025 SPIE.

关键词： Aircraft detection

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Time Series Classification and Forecasting Method Selection in the Task of Intellectual Systems Development

Time Series Classification and Forecasting Method Selection ...

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2024 international Russian automation conference, RusAutoCon 2024

作者： Sergeev, Pavel S. Yarushkina, Nadezhda G. Ulyanovsk State Technical University Information Systems department Ulyanovsk Russia

Nowadays, it is important to implement intellectual analysis methods in information systems of different areas. Processes in such systems are described by temporal data and can be presented in the form of time series. While analyzing those time series, one of the main tasks is forecasting. There are many forecasting methods and models known. This variety poses for a researcher a problem of choice of an efficient one. This paper proposes the model of the forecasting method selection based on valuable time series characteristics. The proposed model can be implemented as a multiclass classifier via neural networks such as a multilayer perceptron. Different multilayer perceptron architectures are presented in this paper. A comparison of their performances using the KFold method is also provided. © 2024 IEEE.

关键词： Prediction models

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A physics-informed, vision-based method to reconstruct all deformation modes in slender bodies 39

A physics-informed, vision-based method to reconstruct all d...

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IEEE international conference on robotics and automation (ICRA)

作者： Kim, Seung Hyun Chang, Heng-Sheng Shih, Chia-Hsien Uppalapati, Naveen Kumar Halder, Udit Krishnan, Girish Mehta, Prashant G. Gazzola, Mattia Univ Illinois Mech Sci & Engn Champaign IL 61801 USA Univ Illinois Coordinated Sci Lab Champaign IL USA

ISBN: (纸本)9781728196817

This paper is concerned with the problem of estimating (interpolating and smoothing) the shape (pose and the six modes of deformation) of a slender flexible body from multiple camera measurements. This problem is important in both biology, where slender, soft, and elastic structures are ubiquitously encountered across species, and in engineering, particularly in the area of soft robotics. The proposed mathematical formulation for shape estimation is physics-informed, based on the use of the special Cosserat rod theory whose equations encode slender body mechanics in the presence of bending, shearing, twisting and stretching. The approach is used to derive numerical algorithms which are experimentally demonstrated for fiber reinforced and cable-driven soft robot arms. These experimental demonstrations show that the methodology is accurate (<5 mm error, three times less than the arm diameter) and robust to noise and uncertainties.

关键词： Uncertainty Three-dimensional displays Smoothing methods Shape Shape measurement Soft robotics Bending

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Applications of Machine Learning methods for Positioning in Scanning Electrochemical Microscope 28th

Applications of Machine Learning Methods for Positioning in ...

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2024 international conference automation

作者： Ivinskij, Vadimas Pandey, Vijitashwa Morkvenaite-Vilkonciene, Inga Vilnius Gediminas Tech Univ LT-10223 Vilnius Lithuania Oakland Univ Rochester MI 48309 USA State Res Inst Ctr Phys Sci & Technol Vilnius Lithuania

ISBN: (纸本)9783031782657;9783031782664

A typical scanning electrochemical microscope (SECM) consists of a three-axis positioning system, usually with stepper motors and potentiostat. Working at a micro and nanoscale, the most consistent problem is vibrations, which an antivibration table can solve. However, this is not a solution for some devices placed in small environments. Therefore, we suggest reducing vibrations by machine learning methods, making SECM more modular. In this paper, we compare and evaluate the use of neural networks within similar systems that have specialized architecture requirements, the use of samples that require a specialized approach to attribute extraction concerning feature fidelity for learning algorithms, control-machine learning combination methods for static and dynamic applications in electrochemical systems as well as application of neural network functions as stand-alone packages. The results were taken for the application of prototyping a tangible and portable electrochemical scanning device system. Potential improvements, such as micro stepping instead of time delay between steps, are considered. The proposed methods in this paper are aimed at countering the disturbances caused by transient processes during actuation and control tuning using non-evasive methods for cheaper solution alternatives.

关键词： Scanning electrochemical systems Machine learning Control systems

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Satellite Remote Sensing Application Model Parallel Training Technology Research

Satellite Remote Sensing Application Model Parallel Training...

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robotics, Control and automation (ICRCA), international conference on

作者： Xuebo Zhang National Key Laboratory of Intelligent Spatial Information College of Aerospace Information Aerospace Engineering University Beijing China

ISBN: (数字)9798331544577

ISBN: (纸本)9798331544584

In response to the long training time of satellite remote sensing application models, this paper first introduces the main methods of parallel training, as well as the support provided by PyTorch for parallel training. Based on this, the target detection model YOLOv4 and the U-Net-based land-sea segmentation model were trained in parallel, and the experiments showed that the training time of both types of models was significantly reduced.

关键词： Training Satellites automation Training data Object detection Parallel processing Data models Remote sensing Robots

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Autonomous Vehicle Parking in Dynamic Environments: An Integrated System with Prediction and Motion Planning 39

Autonomous Vehicle Parking in Dynamic Environments: An Integ...

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IEEE international conference on robotics and automation (ICRA)

作者： Leu, Jessica Wang, Yebin Tomizuka, Masayoshi Di Cairano, Stefano Univ Calif Berkeley Dept Mech Engn Berkeley CA 94720 USA Mitsubishi Elect Res Labs Cambridge MA 02139 USA

ISBN: (纸本)9781728196817

This paper presents an integrated motion planning system for autonomous vehicle (AV) parking in the presence of other moving vehicles. The proposed system includes 1) a hybrid environment predictor that predicts the motions of the surrounding vehicles and 2) a strategic motion planner that reacts to the predictions. The hybrid environment predictor performs short-term predictions via an extended Kalman filter and an adaptive observer. It also combines short-term predictions with a driver behavior cost-map to make long-term predictions. The strategic motion planner comprises 1) a model predictive control-based safety controller for trajectory tracking;2) a search-based retreating planner for finding an evasion path in an emergency;3) an optimization-based repairing planner for planning a new path when the original path is invalidated. Simulation validation demonstrates the effectiveness of the proposed method in terms of initial planning, motion prediction, safe tracking, retreating in an emergency, and trajectory repairing.

关键词： Trajectory tracking Predictive models Observers Planning Trajectory Safety Kalman filters

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Adaptive Dynamic Programming-Based Distributed Coordinated Control for Dual-Arm Reconfigurable Manipulators 6

Adaptive Dynamic Programming-Based Distributed Coordinated C...

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6th international conference on robotics, Control and automation Engineering, RCAE 2023

作者： Liu, Guanting Li, Yuanchun Qu, Meng An, Tianjiao Zhu, Mingchao Ma, Bing Changchun University of Technology Department of Control Science and Engineering Changchun China Changchun Institute of Optics Fine Mechanics and Physics Chinese Academy of Sciences Department of Control Science and Engineering Changchun China

ISBN: (纸本)9798350315301

In this paper, a distributed coordinated control method of dual-arm reconfigurable manipulators based on adaptive dynamic programming is proposed. Through kinematic analysis, the dynamic models of the reconfigurable manipulators and the grasped object are established, respectively. According to the load distribution method, the motion induction force is effectively distributed on each arm. Then, the dynamic model of a single reconfigurable manipulator is obtained. The performance index function is defined to reflect the joint position tracking error and internal force tracking error. The neural network is utilized to approximate the performance index function to obtain the optimal control strategy through the Policy Iteration (PI) algorithm. Thus, a distributed optimal coordinated controller is acquired. The tracking error of the manipulator system is proved be ultimately uniformly bounded (UUB) through Lyapunov's stability. Finally, the effectiveness of the developed control method is verified by simulations. © 2023 IEEE.

关键词： Dynamic models

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Exact-likelihood User Intention Estimation for Scene-compliant Shared-control Navigation 39

Exact-likelihood User Intention Estimation for Scene-complia...

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IEEE international conference on robotics and automation (ICRA)

作者： Katuwandeniya, Kavindie Kiss, Stefan H. Shi, Lei Miro, Jaime Valls Univ Technol Sydney Robot Inst Sydney NSW Australia

ISBN: (纸本)9781728196817

A predictive model for mobility systems capable of understanding the trajectory a user intends to follow in the environment is proposed. Understanding user intention is paramount for any shared-control navigation strategy between a user and an active robotic agent. Equally important however is being able to go beyond simple sample generation to assign probabilistic meaning to the set of possible future trajectories, so most likely scenarios can be assumed. The framework estimates a distribution over possible intentions, proposing a novel generative model predicated on Normalizing Flows which accounts for past behaviours, as traditionally reported in the literature, but also incorporates visual scene information. As the model permits trajectories to be assigned exact likelihoods, tractable density estimates can be readily exploited to finalize an executable intention. Baseline comparisons with the publicly available and widely used KITTI navigational dataset show significant improvements (up to 11.08%) with respect to traditional metrics such as Average and Final Displacement Errors. A novel metric that stands independent of the number of samples is also proposed as a more fitting comparison for future works.

关键词： Measurement Visualization Navigation Fitting Estimation Predictive models Robot sensing systems

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Patent Recommendation methods Based on Transformer Encoders and Regularization Strategies

Patent Recommendation Methods Based on Transformer Encoders ...

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2024 lEEE international conference on Advanced Information, Mechanical Engineering, robotics and automation, AIMERA 2024

作者： Zheng, Wan Ding, Qingyan Li, Na Pan, Yu Dong, Xuecheng Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center National Supercomputer Center in Jinan Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan China

ISBN: (纸本)9798350343335

Patent recommendation service has become a major demand of all walks of life in the society. Patent data collection and analysis can not only help enterprises grasp the development status of the industry, but also provide decision support for them in the research and development process of science and technology services. In view of the problem of inaccurate patent recommendation caused by the inconsistency between the search text and the patent text length and the low accuracy of traditional patent recommendation, a word and sentence dual processing model based on Transformer encoder and regularization strategy is proposed to complete the patent recommendation task from the level of sentence granularity and word granularity. The sentence granularity feature extraction module uses the transformer encoder superimposed multi-head attention method to obtain the feature information between the sentence vector groups, so that the sentence vector fuses the feature information of each sentence, and solves the problem of low recommendation accuracy caused by the inconsistent length of the patent text and the search text;The word granularity feature extraction module uses the twin network model to extract the feature information of the sentence granularity vector using the Transformer encoder using the double splicing operation, improve the model performance and model accuracy;Introduce a strategy based on Dropout and symmetric JS divergence regularization, improve the model generalization ability and accuracy. Experiments have proved that it outperforms SBERT, BERT, MGMSN and SVM models in terms of performance. © 2024 IEEE.

关键词： Patents and inventions

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CrackSegMamba: A Lightweight Mamba Model for Crack Segmentation

CrackSegMamba: A Lightweight Mamba Model for Crack Segmentat...

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2024 IEEE international conference on robotics and Biomimetics, ROBIO 2024

作者： Qi, Weiqing Ma, Fulong Zhao, Guoyang Liu, Ming Ma, Jun Robotics and Autonomous Systems Thrust Guangzhou511453 China

ISBN: (纸本)9781665481090

Crack localization and segmentation are essential for infrastructure maintenance and safety assessments, enabling timely repairs and preventing structural failures. Despite advancements in deep learning, crack segmentation remains challenging due to the need for real-time performance and computational efficiency. Existing methods often rely on large, resource-intensive models, limiting their practical deployment. We introduce CrackSegMamba, a novel model featuring Channel-wise Parallel Mamba (CPM) Modules, which achieves state-of-the-art performance with fewer than 0.23 million parameters and just 0.7 GFLOPs. CrackSegMamba reduces computational cost by 40-fold and parameter count by nearly 100-fold compared to existing models, while maintaining comparable accuracy. These features make CrackSegMamba ideal for real-time applications. Additionally, we present Crack20000, an annotated dataset of 20,000 concrete crack images to support further research and validation. Evaluations on the Crack500 [1] and Crack20000 datasets demonstrate that CrackSegMamba delivers comparable accuracy to leading methods, with significantly reduced computational requirements. Project page is available at: https://***/view/cracksegmamba. © 2024 IEEE.

关键词： Image segmentation

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