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An insulator feature-enhanced segmentation method based on the residual-type attention mechanism

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An insulator feature-enhanced segmentation method based on t...

IEEE Advanced Information Management,Communicates,Electronic and Automation control Conference (IMCEC)

作者： Xin Tang Jianxu Mao Junfei Yi Ziming Tao Zhenyu He College of Electrical and Information Engineering Hunan University Changsha China National Engineering Research Center for Robot Vision Perception and Control Technology Changsha China

ISBN: (纸本)9781665479691

As an crucial component of power line, the timely check for the state of insulator is necessary for the normal running of transmission lines. In view of debasement of insulator segmentation accuracy in current transmission line caused by complex background, low contrast, and the quality of images is not guaranteed, we improve U-Net by combining attention mechanism and residual connection. Residual connection is added to the encoder part to improve the extraction of low-level semantic information, and the attention mechanism is added to the decoder part for integrating high and low level features better and reduce the error between them. We confirm the effectiveness of the improved module through experiments, while the results showing that the improved U-Net model segmentation performance on insulator dataset is improved from 0.875 to 0.912. And our method also outperforms previous segmentation work on insulator dataset.

关键词： Image segmentation Power transmission lines Automation Semantics Insulators Feature extraction Information management

High Dynamic Range 3d Measurement with Adaptive Projection Pattern Generated from Saturated Image

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SSRN

SSRN 2024年

作者： Yu, Hongshan Liang, Xianqiang Yu, Ruiming Xu, Haiqiang Sun, Wei National Engineering Laboratory for Robot Visual Perception and Control Technology College of Electrical and Information Engineering Hunan University Changsha China

Fringe projection profilometry (FPP) shows the advantages of high efficiency and high accuracy in distance sensing. However, it is prone to measurement errors due to fringe saturation in high dynamic range (HDR) scenes. Popular adjust projection intensity (API) based FPP methods have been proposed to reduce the influence of reflectivity variance. Because existing API based FPP method depend on unsaturated images to generate adaptive projection patterns, its measurement time is increased. In this paper, we propose a high dynamic range 3D measurement method with adaptive projection pattern generated from saturated image. Firstly, we establish the reflectance relationship between unsaturated region and saturated region to compute adaptive projection intensity, which removes the dependence of unsaturated images. Secondly, we improve measurement efficiency in computation process by recycling the captured images. In addition, we propose a capture intensity correction method to remove the effect of light propagation distance. Experiments prove that the proposed method has significant advantages in a variety of HDR scenes. Compared with linear-API method, proposed method improves the measurement accuracy by 15.59%. Meanwhile, the number of projection patterns is reduced by 40% compared to the method which based on multiple exposure fusion image (MEFI). © 2024, The Authors. All rights reserved.

关键词： Reflection

Power Grid Fastener Defect Detection Based on Dual Context Information Fusion

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Power Grid Fastener Defect Detection Based on Dual Context I...

IEEE Advanced Information Management,Communicates,Electronic and Automation control Conference (IMCEC)

作者： Liu Qingxian Mao Jianxu Yi Junfei Tao Ziming Yu Junlong College of Electrical and Information Engineering Hunan University Changsha China National Engineering Research Center for Robot Vision Perception and Control Technology Changsha China

ISBN: (纸本)9781665479691

The working state of fasteners is closely related to the safe and stable operation of the power grid. However, the performance of the existing fastener defect detection models is not enough to meet the requirements, because the size of object is small, the environment is complex, the sample size is unbalanced and so on. Therefore, we design a simple but effective feature fusion method, called Double Context Information Enhancement Module. The model can fully learn the features of fasteners by expanding the receptive field of the feature map during the construction of the feature pyramid, and the feature expression ability of fasteners can be enhanced by learning the features of adjacent layers after the construction of the feature pyramid. Through experiments, we proved that DCEM can improve about 8.8AP compared with the original model, and at the same time, the effectiveness and advancement of DCEM are proved by comparing with SOTA.

关键词： Correlation Automation Aggregates Fasteners Feature extraction Power grids Information management

Single-Leader-Dual-Follower Teleoperation in Object-Holding Task with Internal Force Regulation 26

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Single-Leader-Dual-Follower Teleoperation in Object-Holding ...

26th International Conference on Automation and Computing, ICAC 2021

作者： Huang, Darong Jiang, Yiming Yang, Chenguang South China University of Technology School of Automation Science and Engineering Guangzhou510641 China Hunan University National Engineering Laboratory for Robot Visual Perception and Control Changsha410082 China University of the West of England Bristol Robotics Laboratory BristolBS16 1QY United Kingdom

ISBN: (纸本)9781860435577

robot teleoperation attracts growing attention of researchers in many domains. Plenty of factors contribute to the good performance of a smart teleoperation system, and one crucial factor is that it provides an environmental feedback for the operator. Hence, in this paper, we offer a solution to help operator to perceive the contact force of the tele-robot. In addition, we introduce a strategy to cooperatively teleoperate two follower robots by using only single leader1 robot, which helps to reduce the control burden for the operator, as well as enables the teleoperation system to avoid the problem caused by the mismatched degree of freedom (DoF). Moreover, an impedance model based technique is employed to regulate the holding internal force during object-holding tasks. Experiments are conducted gradually, using a Touch X device as the leader robot and two arms of the Baxter robot as the tele-robots. Experimental results validate the feasibility of the proposed teleoperation system. © 2021 Chinese Automation and Computing Society in the UK-CACSUK.

关键词： Remote control

Multi-Expert Adversarial Attack Detection in Person Re-identification Using Context Inconsistency

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Multi-Expert Adversarial Attack Detection in Person Re-ident...

International Conference on Computer vision (ICCV)

作者： Xueping Wang Shasha Li Min Liu Yaonan Wang Amit K. Roy-Chowdhury College of Electrical and Information Engineering Hunan University China National Engineering Laboratory for Robot Visual Perception and Control Technology China University of California

ISBN: (纸本)9781665428132

The success of deep neural networks (DNNs) has promoted the widespread applications of person re-identification (ReID). However, ReID systems inherit the vulnerability of DNNs to malicious attacks of visually in-conspicuous adversarial perturbations. Detection of adversarial attacks is, therefore, a fundamental requirement for robust ReID systems. In this work, we propose a Multi-Expert Adversarial Attack Detection (MEAAD) approach to achieve this goal by checking context inconsistency, which is suitable for any DNN-based ReID systems. Specifically, three kinds of context inconsistencies caused by adversarial attacks are employed to learn a detector for distinguishing the perturbed examples, i.e., a) the embedding distances between a perturbed query person image and its top-K retrievals are generally larger than those between a benign query image and its top-K retrievals, b) the embedding distances among the top-K retrievals of a perturbed query image are larger than those of a benign query image, c) the top-K retrievals of a benign query image obtained with multiple expert ReID models tend to be consistent, which is not preserved when attacks are present. Extensive experiments on the Market1501 and DukeMTMC-ReID datasets show that, as the first adversarial attack detection approach for ReID, MEAAD effectively detects various adversarial attacks and achieves high ROC-AUC (over 97.5%).

关键词： Deep learning Computer vision Perturbation methods Neural networks Detectors Feature extraction Context modeling

Sparse Optical Flow-Based Line Feature Tracking

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arXiv 2022年

作者： Fu, Qiang Yu, Hongshan Ali, Islam Zhang, Hong National Engineering Laboratory for Robot Visual Perception and Control Technology Hunan University China Department of Computing Science University of Alberta Canada

In this paper we propose a novel sparse optical flow (SOF)-based line feature tracking method for the camera pose estimation problem. This method is inspired by the point-based SOF algorithm and developed based on an observation that two adjacent images in time-varying image sequences satisfy brightness invariant. Based on this observation, we redefine the goal of line feature tracking: track two endpoints of a line feature instead of the entire line based on gray value matching instead of descriptor matching. To achieve this goal, an efficient two endpoint tracking (TET) method is presented: first, describe a given line feature with its two endpoints;next, track the two endpoints based on SOF to obtain two new tracked endpoints by minimizing a pixel-level grayscale residual function;finally, connect the two tracked endpoints to generate a new line feature. The correspondence is established between the given and the new line feature. Compared with current descriptor-based methods, our TET method needs not to compute descriptors and detect line features repeatedly. Naturally, it has an obvious advantage over computation. Experiments in several public benchmark datasets show our method yields highly competitive accuracy with an obvious advantage over speed. The source code of our method is released at: https://***/cnqiangfu/TET. Copyright © 2022, The Authors. All rights reserved.

关键词： Optical flows

PL-VINS: Real-Time Monocular Visual-Inertial SLAM with Point and Line Features

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arXiv 2020年

作者： Fu, Qiang Wang, Jialong Yu, Hongshan Ali, Islam Guo, Feng He, Yijia Zhang, Hong National Engineering Laboratory for Robot Visual Perception and Control Technology Hunan University China Department of Computing Science University of Alberta Canada Megvii Technology Beijing China

Leveraging line features to improve localization accuracy of point-based visual-inertial SLAM (VINS) is gaining interest as they provide additional constraints on scene structure. However, real-time performance when incorporating line features in VINS has not been addressed. This paper presents PL-VINS, a real-time and high-efficiency optimization-based monocular VINS method with point and line features, developed based on the state-of-the-art point-based VINS-Mono [1]. We observe that current works use the LSD [2] algorithm to extract line features;however, LSD is designed for scene shape representation instead of the pose estimation problem, which becomes the bottleneck for the real-time performance due to its high computational cost. In this paper, we modify the LSD algorithm by studying a hidden parameter tuning and length rejection strategy. The modified LSD algorithm can run at least three times as fast as LSD. Further, by representing space lines with the Plücker coordinates, the residual error in line estimation is modeled in terms of the point-to-line distance, which is then minimized by iteratively updating the minimum four-parameter orthonormal representation of the Plücker coordinates. Experiments in a public benchmark dataset show that the localization error of our method is 12-16% less than that of VINS-Mono at the same pose update frequency. The source code of our method is available at: https://***/cnqiangfu/PL-VINS. Copyright © 2020, The Authors. All rights reserved.

关键词： Iterative methods

Multi-Expert Adversarial Attack Detection in Person Re-identification Using Context Inconsistency

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arXiv 2021年

作者： Wang, Xueping Li, Shasha Liu, Min Wang, Yaonan Roy-Chowdhury, Amit K. College of Electrical and Information Engineering Hunan University China National Engineering Laboratory for Robot Visual Perception and Control Technology China University of California Riverside United States

The success of deep neural networks (DNNs) has promoted the widespread applications of person reidentification (ReID). However, ReID systems inherit the vulnerability of DNNs to malicious attacks of visually inconspicuous adversarial perturbations. Detection of adversarial attacks is, therefore, a fundamental requirement for robust ReID systems. In this work, we propose a Multi-Expert Adversarial Attack Detection (MEAAD) approach to achieve this goal by checking context inconsistency, which is suitable for any DNN-based ReID systems. Specifically, three kinds of context inconsistencies caused by adversarial attacks are employed to learn a detector for distinguishing the perturbed examples, i.e., a) the embedding distances between a perturbed query person image and its top-K retrievals are generally larger than those between a benign query image and its top-K retrievals, b) the embedding distances among the top-K retrievals of a perturbed query image are larger than those of a benign query image, c) the top-K retrievals of a benign query image obtained with multiple expert ReID models tend to be consistent, which is not preserved when attacks are present. Extensive experiments on the Market1501 and DukeMTMC-ReID datasets show that, as the first adversarial attack detection approach for ReID, MEAAD effectively detects various adversarial attacks and achieves high ROC-AUC (over 97.5%). Copyright © 2021, The Authors. All rights reserved.

关键词： Deep neural networks

P2U-SLAM: A Monocular Wide-FoV SLAM System Based on Point Uncertainty and Pose Uncertainty

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arXiv 2024年

作者： Zhang, Yufan Yang, Kailun Wang, Ze Wang, Kaiwei The State Key Laboratory of Extreme Photonics and Instrumentation Zhejiang University Hangzhou310027 China The School of Robotics The National Engineering Research Center of Robot Visual Perception and Control Technology Hunan University Changsha410012 China The National Engineering Research Center of Robot Visual Perception and Control Technology Hunan University Changsha410082 China

This paper presents P2U-SLAM, a visual Simultaneous Localization And Mapping (SLAM) system with a wide Field of View (FoV) camera, which utilizes pose uncertainty and point uncertainty. While the wide FoV enables considerable repetitive observations of historical map points for matching cross-view features, the data properties of the historical map points and the poses of historical keyframes have changed during the optimization process. The neglect of data property changes triggers the absence of a partial information matrix in optimization and leads to the risk of long-term positioning performance degradation. The purpose of our research is to reduce the risk of the wide field of view visual input to the SLAM system. Based on the conditional probability model, this work reveals the definite impact of the above data properties changes on the optimization process, concretizes it as point uncertainty and pose uncertainty, and gives a specific mathematical form. P2U-SLAM respectively embeds point uncertainty and pose uncertainty into the tracking module and local mapping, and updates these uncertainties after each optimization operation including local mapping, map merging, and loop closing. We present an exhaustive evaluation in 27 sequences from two popular public datasets with wide-FoV visual input. P2U-SLAM shows excellent performance compared with other state-of-the-art methods. The source code will be made publicly available at https://***/BambValley/P2U-SLAM. Copyright © 2024, The Authors. All rights reserved.

关键词： Mapping