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Differentiable Automatic Data Augmentation by Proximal Update for Medical Image Segmentation

IEEE/CAA Journal of Automatica Sinica 2022年第7期9卷 1315-1318页

作者： Wenxuan He Min Liu Yi Tang Qinghao Liu Yaonan Wang College of Electrical and Information Engineering Hunan UniversityChangsha 410082 National Engineering Research Center of Robot Visual Perception and Control Technology Changsha 410082China

Dear editor,This letter presents an automatic data augmentation algorithm for medical image *** increase the scale and diversity of medical images,we propose a differentiable automatic data augmentation algorithm based on proximal update by finding an optimal augmentation ***,on the one hand,a dedicated search space is designed for the medical image segmentation *** the other hand,we introduce a proximal differentiable gradient descent strategy to update the data augmentation policy,which would increase the searching *** of the experiments indicate that the proposed algorithm significantly outperforms state-of-the-art methods,and search speed is 10 times faster than state-of-the-art methods.

关键词： editor policy Image

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Diff9D: Diffusion-Based Domain-Generalized Category-Level 9-DoF Object Pose Estimation

arXiv

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arXiv 2025年

作者： Liu, Jian Sun, Wei Yang, Hui Deng, Pengchao Liu, Chongpei Sebe, Nicu Rahmani, Hossein Mian, Ajmal National Engineering Research Center for Robot Visual Perception and Control Technology College of Electrical and Information Engineering School of Robotics State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body Hunan University Changsha410082 China Institute of Artificial Intelligence and Robotics Xi’an Jiaotong University Xi’an710049 China Department of Information Engineering and Computer Science University of Trento Trento38123 Italy School of Computing and Communications Lancaster University LA1 4YW United Kingdom Department of Computer Science The University of Western Australia WA6009 Australia

Nine-degrees-of-freedom (9-DoF) object pose and size estimation is crucial for enabling augmented reality and robotic manipulation. Category-level methods have received extensive research attention due to their potential for generalization to intra-class unknown objects. However, these methods require manual collection and labeling of large-scale real-world training data. To address this problem, we introduce a diffusion-based paradigm for domain-generalized category-level 9-DoF object pose estimation. Our motivation is to leverage the latent generalization ability of the diffusion model to address the domain generalization challenge in object pose estimation. This entails training the model exclusively on rendered synthetic data to achieve generalization to real-world scenes. We propose an effective diffusion model to redefine 9-DoF object pose estimation from a generative perspective. Our model does not require any 3D shape priors during training or inference. By employing the Denoising Diffusion Implicit Model, we demonstrate that the reverse diffusion process can be executed in as few as 3 steps, achieving near real-time performance. Finally, we design a robotic grasping system comprising both hardware and software components. Through comprehensive experiments on two benchmark datasets and the real-world robotic system, we show that our method achieves state-of-the-art domain generalization performance. Our code will be made public at https://***/CNJianLiu/Diff9D. Copyright © 2025, The Authors. All rights reserved.

关键词： Augmented reality

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LDFCDet: Boosting 3D Object Detectors with Low-High level Feature Crosses Using Laplace Distribution

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IEEE Transactions on Circuits and Systems for Video technology 2025年

作者： He, Zhenyu Mao, Jianxu Wang, Yaonan Yu, Junlong Tao, Ziming Yi, Junfei Zhang, Hui Wang, Shaoyuan Hunan University College of Electrical and Information Engineering Hunan Changsha 410012 China Hunan University School of Robotics Changsha 410082 China National Engineering Reseach Center for Robot Visual Perception and Control Technology Changsha 410082 China

Highly accurate 3D object detection is critical for autonomous driving and robotic sensing system. However, some objects with few foreground points significantly affect the accuracy of 3D object detection. As the network depth increases, the low-level features of these objects are gradually lost, especially for the hard object. Due to this issue, current LiDAR-only based and multimodal methods often misclassify background as foreground. Therefore, how to leverage the low-level feature that contain information about these objects in the high layer of the network becomes the key to optimizing the issue. In this paper, we propose LDFCDet, a framework boosting 3D object detectors with low-high level feature crosses using Laplace distribution(LD). In our proposed method, we design a low-high level feature crosses module(LHFCM) to embed low-level feature into high-level feature in the deeper layer of the network, and use Laplace distribution to obtain a new low-high level feature that includes information about these objects with few foreground points. In addition, we propose a res-gated feature aggregation module(RGFAM) to fuse the mutli-scale features. Our approach is well-suited for both LiDAR-based and multimodal *** evaluate the LDFCDet on the widely used KITTI dataset, and our method outperforms almost current 3D object detection methods on the challenging KITTI test set. Moreover, we conducted comparative experiments on the ONCE dataset, and the results further demonstrate the effectiveness and superiority of our method. © 1991-2012 IEEE.

关键词： 3D object detection feature aggregation feature crosses Laplace distribution multimodal robotic sensing system

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Motion Planning for Mobile robots with Temporal Logic Specifications 8

Motion Planning for Mobile Robots with Temporal Logic Specif...

引用

8th IEEE International Conference on Advanced robotics and Mechatronics, ICARM 2023

作者： Zeng, Jianxin Wang, Yaonan Miao, Zhiqiang Wang, Sifei College of Electrical and Information Engineering The National Engineering Research Center of Robot Visual Perception and Control Technology Hunan University Changsha410082 China

ISBN: (纸本)9798350300178

In this paper, we propose a motion planning method for mobile robots in order to satisfy task requirements specified in linear temporal logic (LTL). The proposed method follows the traditional hierarchical planning workflow, including path planning and trajectory planning. Firstly, we propose a path planning method based on rapidly exploring random trees (RRT*) and Dubins curve. The tree derived from RRT∗ is expanded with the help of Buchi automaton converted by an LTL formula. The proposed method can automatically adjust the iterations of sampling according to the scale of Buchi automata. Then, we formulate an optimization problem for the generation of trajectory. We use Bezier curve to express the trajectory and use differential flatness of mobile robots to simplify the optimization problem. Finally, we validate the practicality of our method through simulations and real-world experiments. © 2023 IEEE.

关键词： Mobile robots

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A Mobile robotic System for Data center Thermal Environment Measurement and Reconstruction

A Mobile Robotic System for Data Center Thermal Environment ...

引用

2021 China Automation Congress, CAC 2021

作者： Qin, Ronghan Fang, Qiu Wang, Yaonan The National Engineering Laboratory for Robot Visual Perception and Control Technology Hunan University Hunan Changsha410082 China

ISBN: (纸本)9781665426473

Data centers are the key infrastructure for information services. The monitoring of the thermal environment of the data center computer room is an important work for its safe operation. This paper proposed a mobile robotic system for data center thermal environment measurement, which can freely sample the temperature and humidity data around the facilities within the computer room. On one hand, measuring the temperature and humidity in a mobile way can obtain far more environmental data than fixed-point sensors. On the other hand, the mobile robot can be used instead of manual inspection. In addition, a temperature field reconstruction method is provided based on the sampled temperature data. Theoretically, the temperature of locations that have not been measured can be evaluated by interpolating sample temperature around them. The reconstruction of the temperature field can present the variation of the temperature more clearly and help find the hot spots or locations with low cooling efficiency during the operation. Lastly, experiments are carried out to study the measurement error of the mobile robotic system and an error correction method is proposed. After that, the relationship between the temperature reconstruction error and the layout of sampling points is investigated. © 2021 IEEE

关键词： Interpolation

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OAFuser: Toward Omni-Aperture Fusion for Light Field Semantic Segmentation

IEEE Transactions on Artificial Intelligence

引用

IEEE Transactions on Artificial Intelligence 2024年第12期5卷 6225-6239页

作者： Teng, Fei Zhang, Jiaming Peng, Kunyu Wang, Yaonan Stiefelhagen, Rainer Yang, Kailun Hunan University School of Robotics National Engineering Research Center of Robot Visual Perception and Control Technology Changsha410082 China Karlsruhe Institute of Technology Institute for Anthropomatics and Robotics Karlsruhe76131 Germany Institute for Visual Computing ETH Zurich Zurich8092 Switzerland

Light field cameras are capable of capturing intricate angular and spatial details. This allows for acquiring complex light patterns and details from multiple angles, significantly enhancing the precision of image semantic segmentation. However, two significant issues arise: 1) The extensive angular information of light field cameras contains a large amount of redundant data, which is overwhelming for the limited hardware resources of intelligent agents. 2) A relative displacement difference exists in the data collected by different microlenses. To address these issues, we propose an omni-aperture fusion model (OAFuser) that leverages dense context from the central view and extracts the angular information from subaperture images to generate semantically consistent results. To simultaneously streamline the redundant information from the light field cameras and avoid feature loss during network propagation, we present a simple yet very effective subaperture fusion module (SAFM). This module efficiently embeds subaperture images in angular features, allowing the network to process each subaperture image with a minimal computational demand of only (∼1\rm GFlops). Furthermore, to address the mismatched spatial information across viewpoints, we present a center angular rectification module (CARM) to realize feature resorting and prevent feature occlusion caused by misalignment. The proposed OAFuser achieves state-of-the-art performance on four UrbanLF datasets in terms of all evaluation metrics and sets a new record of 84.93% in mIoU on the UrbanLF-Real Extended dataset, with a gain of +3.69%. The source code for OAFuser is available at https://***/FeiBryantkit/OAFuser. © 2020 IEEE.

关键词： Semantic Segmentation

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Double-layer Descriptors and High-dimensional Search Mechanisms for Partial Point Cloud Registration

Double-layer Descriptors and High-dimensional Search Mechani...

引用

2023 China Automation Congress, CAC 2023

作者： Wang, Zhiyu Fang, Qiu Wang, Yaonan Lu, Hong Ye, Jun National Engineering Research Center for Robot Visual Perception and Control Technology School of Electrical and Information Engineering Hunan University Hunan Changsha410082 China

ISBN: (纸本)9798350303759

Partial point cloud registration is an essential preprocessing technique to generate complete 3D shapes that aim to transform partial scans into a common coordinate system. Existing methods that utilize geometric alignment rarely combine local and global information, which results in many incorrect correspondences. In the paper, a two-layer descriptor and a high-dimension searching approach (DDHSM) are utilized to extract and select the correct correspondences. Firstly, in the first layer, the Fast Point Feature Histogram (FPFH) is used to describe the feature of every point with high-dimension information based on its robustness and fastness. Then a high-dimensional search is considered to extract the initial corresponding set. In the second layer, global information is introduced to find the correct correspondences. For each point pair in the initial corresponding set, the normal angles are compared for all remaining points that satisfy the distance requirement. Finally, the singular value decomposition (SVD) method is implemented to compute the rigid transformation based on the updated corresponding set. Experimental results and comparisons with state-of-the-art methods demonstrate the effectiveness and feasibility of our approach. © 2023 IEEE.

关键词： Graphic methods

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MonoDiff9D: Monocular Category-Level 9D Object Pose Estimation via Diffusion Model

arXiv

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arXiv 2025年

作者： Liu, Jian Sun, Wei Yang, Hui Zheng, Jin Geng, Zichen Rahmani, Hossein Mian, Ajmal National Engineering Research Center for Robot Visual Perception and Control Technology College of Electrical and Information Engineering China School of Robotics Hunan University Changsha410082 China Central South University Changsha410082 China Lancaster University LA1 4YW United Kingdom The University of Western Australia WA6009 Australia

Object pose estimation is a core means for robots to understand and interact with their environment. For this task, monocular category-level methods are attractive as they require only a single RGB camera. However, current methods rely on shape priors or CAD models of the intra-class known objects. We propose a diffusion-based monocular category-level 9D object pose generation method, MonoDiff9D. Our motivation is to leverage the probabilistic nature of diffusion models to alleviate the need for shape priors, CAD models, or depth sensors for intra-class unknown object pose estimation. We first estimate coarse depth via DINOv2 from the monocular image in a zero-shot manner and convert it into a point cloud. We then fuse the global features of the point cloud with the input image and use the fused features along with the encoded time step to condition MonoDiff9D. Finally, we design a transformer-based denoiser to recover the object pose from Gaussian noise. Extensive experiments on two popular benchmark datasets show that MonoDiff9D achieves state-of-the-art monocular category-level 9D object pose estimation accuracy without the need for shape priors or CAD models at any stage. Our code will be made public at https://***/CNJianLiu/MonoDiff9D. Copyright © 2025, The Authors. All rights reserved.

关键词： Image coding

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An Intelligent Route Recommendation Method for Smart Internet-Connected Electric Vehicles Under the Coupling of Traffic and Electricity 42

An Intelligent Route Recommendation Method for Smart Interne...

引用

42nd Chinese control Conference, CCC 2023

作者： Xie, Jinhuan Liu, Dongqi Yang, Zhenwu School of Electrical and Information Engineering Changsha University of Science and Technology Hunan 410114 China National Engineering Research Center for Robot Visual Perception and Control Technology Hunan 410012 China

ISBN: (纸本)9789887581543

Aiming at the charging and navigation strategy of electric vehicles in the road-electricity coupling scenario, this paper proposes a hybrid planning travel scheme based on the pre-charging and charging warning model based on the road traffic congestion predicted by the big data of the Internet of Vehicles. This model integrally balances the interests of users and power grid in the coupled road-electric network, and solves the optimal path scheme under different travel scenarios based on collaborative filtering algorithm and user feedback, considering the influence of vehicle departure time and remaining power in short time scale. It is experimentally demonstrated that the proposed route hybrid recommendation model has better planning effect compared with the single travel model. © 2023 Technical Committee on control Theory, Chinese Association of Automation.

关键词： Roads and streets

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A lightweight intrusion detection method for intelligent substations 3

A lightweight intrusion detection method for intelligent sub...

引用

3rd International Conference on Electronic Information engineering and Data Processing, EIEDP 2024

作者： Zhang, Zidong Liu, Dongqi Liang, Haolan School of Electrical and Information Engineering Changsha University of Science and Technology Hunan410114 China National Engineering Research Center for Robot Visual Perception and Control Technology Hunan410012 China

ISBN: (纸本)9781510680531

Aiming at the current problem of lack of effective data and high service latency for intrusion detection in smart substations, this paper proposes a lightweight intrusion detection method for smart substations. Through the federated learning method and knowledge distillation method this paper trains a lightweight intrusion detection model based on LSTM. Experiments show that this paper's method has a large improvement in the accuracy and service delay on the NSL-KDD dataset. © 2024 SPIE.

关键词： Distillation

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