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Infrared Dim and Small Target Detection and Tracking Based on Single Multi-Frame Algorithm under Sea Clutter Background

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Infrared Dim and Small Target Detection and Tracking Based o...

第三十八届中国控制会议

作者： Ting Zhao Taoshen Wang Yaoxin Cao Yunze Cai Key Laboratory of System Control and Information Processing Ministry of Education Department of Automation Shanghai Jiao Tong University Key Laboratory of Marine Intelligent Equipment and System of Ministry of Education Department of Automation Shanghai Jiao Tong University Shanghai Institute of Spaceflight Control Technology

Infrared dim and small target detection and track under sea clutter is significant and crucial in the infrared image process. It is a difficult task because of the disturbing clutter, the changing scenario and weak targets. In this paper, a complete process to detect and track dim small targets is proposed. Firstly, we use Mean and 3 D Filtering with Block-Matching(M3 DBM)algorithm to denoise infrared images. Inspired by the well-known BM3 D algorithm and NLM algorithm, M3 DBM can keep small targets from impair while denoising. Then, we use Butterworth high-pass filter and morphological filter to extract possible targets from single frame image. Finally, an optimal pipeline filtering algorithm(OPF) based on target position prediction and multi-target stable track is proposed. Optimal means that choose the best pipe for target and vice versa. OPF can track targets stably and accurately. The experimental results show that the proposed algorithm achieves good results in both accuracy and efficiency.

关键词： Infrared dim and small targets Sea clutter background M3DBM denoising Butterworth high-pass morphological Optimal pipeline filtering

Adaptive Clustering SOFC Image Segmentation Based on Particle Swarm Optimization

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Adaptive Clustering SOFC Image Segmentation Based on Particl...

2019 3rd International Conference on Machine Vision and information technology (CMVIT 2019)

作者： Xuefei Yang Xiaowei Fu Xi Li College of Computer Science and Technology Wuhan University of Science and TechnologyHubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System State Key Laboratory of Materials Processing and Die & Mould Technology Huazhong University of Science and Technology College of Automation Huazhong University of Science and Technology

Microstructural parameters are important for analyzing the chemistry and performance of solid oxide fuel cells（SOFCs）. Aiming at the YSZ/Ni anode optical microscopy（OM） image of SOFC, in this paper, particle swarm intelligent optimization algorithm is used to improve the fuzzy C-means clustering algorithm for image segmentation. Particle swarm optimization is used to adaptively search the initial clustering center, helping to avoid local optimization and preserve more image detail. The experimental results show that the proposed method can improve the segmentation accuracy of images. At the same time, it can accurately segment the SOFC three-phase and provide effective image segmentation results for the microstructure parameters.

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A registration-aided domain adaptation network for 3D Point cloud based place recognition

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arXiv 2020年

作者： Qiao, Zhijian Hu, Hanjiang Shi, Weiang Chen, Siyuan Liu, Zhe Wang, Hesheng Department of Automation Insititue of Medical Robotics Key Laboratory of System Control and Information Processing of Ministry of Education Key Laboratory of Marine Intelligent Equipment and System of Ministry of Education Shanghai Jiao Tong University Shanghai200240 China Beijing Advanced Innovation Center for Intelligent Robots and Systems Beijing Institute of Technology China Department of Mechanical Engineering Carnegie Mellon University United States Department of Computer Science and Technology University of Cambridge United Kingdom

In the field of large-scale SLAM for autonomous driving and mobile robotics, 3D point cloud based place recognition has aroused significant research interest due to its robustness to changing environments with drastic daytime and weather variance. However, it is time-consuming and effort-costly to obtain high-quality point cloud data for place recognition model training and ground truth for registration in the real world. To this end, a novel registration-aided 3D domain adaptation network for point cloud based place recognition is proposed. A structure-aware registration network is introduced to help to learn features with geometric information and a 6-DoFs pose between two point clouds with partial overlap can be estimated. The model is trained through a synthetic virtual LiDAR dataset through GTA-V with diverse weather and daytime conditions and domain adaptation is implemented to the real-world domain by aligning the global features. Our results outperform state-of-the-art 3D place recognition baselines or achieve comparable on the real-world Oxford RobotCar dataset with the visualization of registration on the virtual dataset. Copyright © 2020, The Authors. All rights reserved.

关键词： Three dimensional computer graphics

Vehicle detection based on point cloud intensity and distance clustering

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Journal of Physics: Conference Series 2021年第4期1748卷

作者： Zhao Wang Xing Wang Bin Fang Kun Yu Jie Ma School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan 430074 China National key laboratory of multispectral information processing technology Huazhong University of Science and Technology Wuhan 430074 China Science and technology on complex system control and intelligent agent cooperation laboratory Beijing 100074 China

In the intelligent transportation system, vehicle detection is one of the essential technologies in obstacle avoidance and navigation, however the existing vehicle detection methods cannot meet the actual needs. This paper presents a vehicle detection method combines the intensity and distance information of point cloud, which improves the segmentation performance of nearby objects. Specifically, the data of point cloud collected by lidar is preprocessed first. Then the processed point cloud is clustered by combining its coordinate and intensity information. Finally, the clustered suspected targets are fed to the random forest classifier. Our method can efficiently detect and classify targets in large-scale disordered 3D point cloud with high accuracy. In the real-scanned Livox Mid-40 Lidar dataset, our proposed method improves the detection accuracy by 31% compared with the traditional Euclidean clustering.

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Predefined-time terminal sliding mode control of robot manipulators

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arXiv 2020年

作者： Liang, Chang-Duo Ge, Ming-Feng Liu, Zhi-Wei Wang, Yan-Wu Karimi, Hamid Reza School of Mechanical Engineering and Electronic Information China University of Geosciences Wuhan430074 China School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China Key Laboratory of Image Processing and Intelligent Control Ministry of Education Huazhong University of Science and Technology Wuhan430074 China Department of Mechanical Engineering Politecnico di Milano Milan20156 Italy

In this paper, we present a new terminal sliding mode control to achieve predefined-time stability of robot manipulators. The proposed control is developed based on a novel predefined-time terminal sliding mode (PTSM) surface, on which the states are forced to reach the origin in a predefined time, i.e., the settling time is independent to the initial condition and can be explicitly user-defined via adjusting some specific parameters called the predefined-time parameters. It is also demonstrated that the proposed control can provide satisfactory steady-state performance in the case of both external disturbances and parametric uncertainties. Besides, we present a formal systemic analysis method to derive the sufficient conditions for guaranteeing the predefined-time convergence of the closed-loop system. Finally, the effectiveness and performance of the presented control scheme are illustrated through both theoretical comparisons and numerical simulations. Copyright © 2020, The Authors. All rights reserved.

关键词： Sliding mode control

Anchor-based spatio-temporal attention 3D convolutional networks for dynamic 3D point cloud sequences

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arXiv 2020年

作者： Wang, Guangming Chen, Muyao Liu, Hanwen Yang, Yehui Liu, Zhe Wang, Hesheng The Department of Automation Institute of Medical Robotics Key Laboratory of System Control and Information Processing Ministry of Education Key Laboratory of Marine Intelligent Equipment System of Ministry of Education Shanghai Jiao Tong University Shanghai200240 China Beijing Advanced Innovation Center for Intelligent Robots and Systems Beijing Institute of Technology China The Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai200240 China The Department of Computer Science and Technology University of Cambridge

With the rapid development of measurwement technology, LiDAR and depth cameras are widely used in the perception of the 3D environment. Recent learning based methods for robot perception most focus on the image or video, but deep learning methods for dynamic 3D point cloud sequences are underexplored. Therefore, developing efficient and accurate perception method compatible with these advanced instruments is pivotal to autonomous driving and service robots. An Anchor-based Spatio-Temporal Attention 3D Convolution operation (ASTA3DConv) is proposed in this paper to process dynamic 3D point cloud sequences. The proposed convolution operation builds a regular receptive field around each point by setting several virtual anchors around each point. The features of neighborhood points are firstly aggregated to each anchor based on the spatio-temporal attention mechanism. Then, anchor-based 3D convolution is adopted to aggregate these anchors' features to the core points. The proposed method makes better use of the structured information within the local region and learns spatio-temporal embedding features from dynamic 3D point cloud sequences. Anchor-based Spatio-Temporal Attention 3D Convolutional Neural Networks (ASTA3DCNNs) are built for classification and segmentation tasks based on the proposed ASTA3DConv and evaluated on action recognition and semantic segmentation tasks. The experiments and ablation studies on MSRAction3D and Synthia datasets demonstrate the superior performance and effectiveness of our method for dynamic 3D point cloud sequences. Our method achieves the state-of-the-art performance among the methods with dynamic 3D point cloud sequences as input on MSRAction3D and Synthia datasets. Copyright © 2020, The Authors. All rights reserved.

关键词： Embeddings

Class function-based adaptive disturbance observer for uncertain nonlinear systems

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International Journal of Systems Science 2025年第4期56卷

作者： Ke Shao Jinchao Shao Chunsheng He Runze Hu a School of Civil Aviation Northwestern Polytechnical University (NPU) Xi'an People's Republic of Chinab Rescarch & Development Institute of NPU in Shenzhen Shenzhen People's Republic of Chinac Yangtze River Delta Research Institute of NPU Taicang People's Republic of China d Shandong Key Laboratory of Industrial Control Technology School of Automation Qingdao University Qingdao People's Republic of China a School of Civil Aviation Northwestern Polytechnical University (NPU) Xi'an People's Republic of China e School of Information & Electronics Beijing Institute of Technology Beijing People's Republic of China

A novel design framework of adaptive disturbance observer for a class of uncertain nonlinear systems based on class𝒦functions is proposed in this paper. In the proposed observer, the upper bounds of the disturbance and its derivatives are not required a priori and the inherent adaptive gain can be arbitrarily selected as any class𝒦functions. It is verified that the estimation error converges into a small neighbourhood around zero asymptotically, wherein the size of the above region can be exactly suppressed to be arbitrarily small by tuning parameters. Compared to conventional methods, the proposed observer is of easy implementations with simple structure and less parameters for tuning. Comparison simulation and an illustrating example are conducted to illustrate the superiorities and effectiveness of the proposed observer.

关键词： Adaptive observer class K function nonlinear system upper bound

A Bus-Based Battery Equalization via Modified Isolated Cuk Converter Governed by Adaptive control

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A Bus-Based Battery Equalization via Modified Isolated Cuk C...

Chinese automation Congress (CAC)

作者： Hao Zhong Jianwei Li Ya-Xiong Wang Key Laboratory of Industrial Automation Control Technology and Information Processing (Fuzhou University) Fujian Province University National Engineering Laboratory for Electric Vehicles Beijing Institute of Technology Beijing P. R. China School of Mechanical Engineering and Automation Fuzhou University Fuzhou P. R. China

Active equalization systems require balancing current regulation to further improve balancing efficiency and battery cycle life. This paper focuses on an adaptive control strategy for a battery equalization system based on modified isolated Cuk converter. Firstly, the analysis and modeling of the modified isolated Cuk-based equalizer are presented, and then the development of the adaptive sliding-mode control (SMC) scheme is proposed. The designed controller is verified through testing transient response, reference current variation and load change under continuous conduction mode. Compared with a traditional SMC, the proposed adaptive control has strongly ability to cope with the external disturbance and system parameters variation. A bus-based battery equalization system for five cells in series with an unbalanced state-of-charge (SOC) 0.75, 0.90, 0.45, 0.80 and 0.67 is performed to test the proposed control method. The simulation results indicate that the battery pack reaches to a balanced state in 712 sec.

关键词： Batteries Adaptive control Equalizers Capacitors Inductors Current control Stability analysis

Learning of long-horizon sparse-reward robotic manipulator tasks with base controllers

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arXiv 2020年

作者： Wang, Guangming Xin, Minjian Wu, Wenhua Liu, Zhe Wang, Hesheng Department of Automation Key Laboratory of System Control and Information Processing of Ministry of Education Key Laboratory of Marine Intelligent Equipment and System of Ministry of Education Shanghai Engineering Research Center of Intelligent Control and Management Shanghai Jiao Tong University Shanghai200240 China Department of Computer Science and Engineering University of California San Diego La Jolla CA92093 United States Department of Computer Science and Technology University of Cambridge CambridgeCB2 1TN United Kingdom

Deep Reinforcement Learning (DRL) enables robots to perform some intelligent tasks end-to-end. However, there are still many challenges for long-horizon sparse-reward robotic manipulator tasks. On the one hand, a sparse-reward setting causes exploration inefficient. On the other hand, exploration using physical robots is of high cost and unsafe. In this paper, we propose a method of learning long-horizon sparse-reward tasks utilizing one or more existing traditional controllers named base controllers in this paper. Built upon Deep Deterministic Policy Gradients (DDPG), our algorithm incorporates the existing base controllers into stages of exploration, value learning, and policy update. Furthermore, we present a straightforward way of synthesizing different base controllers to integrate their strengths. Through experiments ranging from stacking blocks to cups, it is demonstrated that the learned state-based or image-based policies steadily outperform base controllers. Compared to previous works of learning from demonstrations, our method improves sample efficiency by orders of magnitude and improves the performance. Overall, our method bears the potential of leveraging existing industrial robot manipulation systems to build more flexible and intelligent controllers. Copyright © 2020, The Authors. All rights reserved.

关键词： Deep learning