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Intervention AUVs: The next challenge 19

Intervention AUVs: The next challenge

引用

19th IFAC World Congress on International Federation of Automatic Control, IFAC 2014

作者： Ridao, Pere Carreras, Marc Ribas, David Sanz, Pedro J. Oliver, Gabriel Computer Vision and Robotics Research Institute Scientific and Technological Park University of Girona CIRS Building C/Pic de Peguera Girona CataloniaCO 17071 Spain Av. Vicent Sos Banyat S/N Castelln12006 Spain Cra. Valldemossa Palma07122 Spain

ISBN: (纸本)9783902823625

While commercially available AUVs are routinely used in survey missions, a new set of applications exists which clearly demand intervention capabilities. The maintenance of: permanent observatories underwater;submerged oil wells;cabled sensor networks;pipes;and the deployment and recovery of benthic stations are but a few of them. These tasks are addressed nowadays using manned submersibles or work-class ROVs, equipped with teleoperated arms under human supervision. Although researchers have recently opened the door to future I-AUVs, a long path is still necessary to pave the way to underwater intervention applications performed in an autonomous way. This paper reviews the evolution timeline in autonomous underwater intervention systems. Milestone projects in the state of the art are reviewed, highlighting their principal contributions to the field. To the best of the authors knowledge only three vehicles have demonstrated some autonomous intervention capabilities so far: ALIVE, SAUVIM and GIRONA 500 I-AUV. Next, GIRONA 500 I-AUV is presented and its software architecture discussed. Recent results in different scenarios are reported: 1) Valve turning and connector plugging/unplugging while docked to a sub-sea panel, 2) Free floating valve turning using learning by demonstration, and 3) Free floating multipurpose multisensory based object recovery. The paper ends discussing the lessons learned so far and presenting the authors view of the future. © IFAC.

关键词： Autonomous underwater vehicles

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Robust text line detection in equipment nameplate images

Robust text line detection in equipment nameplate images

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2019 IEEE International Conference on robotics and Biomimetics, ROBIO 2019

作者： Lai, Jiangyu Guo, Lanqing Qiao, Yu Chen, Xiaolong Zhang, Zhengfu Liu, Canping Li, Ying Fu, Bin Guangzhou Power Supply Bureau Co. Ltd. Guangzhou China ShenZhen Key Lab of Computer Vision and Pattern Recognition SIATSenseTime Joint Lab Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences China SIAT Branch Shenzhen Institute of Artificial Intelligence and Robotics for Society China

ISBN: (纸本)9781728163215

Scene text detection for equipment nameplates in the wild is important for equipment inspection robot since it enables inspection robot to take specific actions for different equipment's. Although text detection in images has achieved great progress in recent years, the detection for equipment nameplates faces several challenges such as extreme illumination and distortion which significantly decrease the detection performance. In this paper, we propose a deep text detection model Robust Text Line Detection (RTLD) for locating word level text instances in equipment cards. Specifically, the proposed model first employs a corner detection module to determine the four corner points of each nameplate, and then a carefully designed image transformed module transforms the irregular nameplate region into a rectangular region. Finally, text detection module is introduced to locate every word level text instance in the transformed images. We conduct extensive experiments to examine our proposed methods on real equipment nameplate images. Our model achieves 91.2% precision and 92.6% recall on Equipment Nameplate Dataset. The experimental results demonstrate the effectiveness of our models. © 2019 IEEE.

关键词： Nameplates

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Understanding Adversarial Examples From the Mutual Influence of Images and Perturbations

Understanding Adversarial Examples From the Mutual Influence...

引用

Conference on computer vision and Pattern Recognition (CVPR)

作者： Chaoning Zhang Philipp Benz Tooba Imtiaz In So Kweon Robotics and Computer Vision (RCV) Laboratory Korea Advanced Institute of Science and Technology (KAIST) Daejeon Korea Korea Advanced Institute of Science and Technology Daejeon South Korea

ISBN: (数字)9781728171685

ISBN: (纸本)9781728171692

A wide variety of works have explored the reason for the existence of adversarial examples, but there is no consensus on the explanation. We propose to treat the DNN logits as a vector for feature representation, and exploit them to analyze the mutual influence of two independent inputs based on the Pearson correlation coefficient (PCC). We utilize this vector representation to understand adversarial examples by disentangling the clean images and adversarial perturbations, and analyze their influence on each other. Our results suggest a new perspective towards the relationship between images and universal perturbations: Universal perturbations contain dominant features, and images behave like noise to them. This feature perspective leads to a new method for generating targeted universal adversarial perturbations using random source images. We are the first to achieve the challenging task of a targeted universal attack without utilizing original training data. Our approach using a proxy dataset achieves comparable performance to the state-of-the-art baselines which utilize the original training dataset.

关键词： Perturbation methods Correlation Training data Feature extraction Training Task analysis Robustness

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Mean field theory and MT neurons

Mean field theory and MT neurons

引用

IEEE International Conference on Systems, Man and Cybernetics

作者： A. Dobbins S.W. Zucker Computer Vision and Robotics Laboratory Department of Electrical Engineering McGill University Montreal QUE Canada Canadian Institute for Advanced Research Montreal QUE Canada

The problem of obtaining reliable estimates of the optic flow field and its variation is discussed. It is argued that circuitry in the middle temporal (MT) visual area is arranged to provide mean field approximations of the optical flow field, that is, the maximal neighborhood over which the local field can be well-approximated as uniform, parallel translation. In addition, the way the mean field approximation varies with neighborhood size and position is informative about the variation of the flow field. It is shown how receptive fields can be synthesized to represent the first-order differential information about the flow field. The same ideas are applicable to the binocular disparity vector field, and lead one to predict the existence of extrastriate neurons that are specialized for the local shear and stretch of the disparity and motion vector fields.< >

关键词： Neurons Image motion analysis Circuits computer architecture computer vision Robot vision systems Laboratories Shape Optical devices Retina

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Orientation robust scene text recognition in natural scene

Orientation robust scene text recognition in natural scene

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2019 IEEE International Conference on robotics and Biomimetics, ROBIO 2019

作者： Chen, Xiaolong Zhang, Zhengfu Qiao, Yu Lai, Jiangyu Jiang, Jian Zhang, Zeyu Fu, Bin Guangzhou Power Supply Bureau Co. Ltd. Guangzhou China ShenZhen Key Lab of Computer Vision and Pattern Recognition SIAT-SenseTime Joint Lab Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences China SIAT Branch Shenzhen Institute of Artificial Intelligence and Robotics for Society China

ISBN: (纸本)9781728163215

In recent years, scene text recognition has achieved significant improvement and various state-of-the-art recognition approaches have been proposed. This paper focused on recognizing text in natural photos of equipment nameplates, which has wide applications in industrial automations. This task only receives little attentions in previous works. The challenge of this problem comes from multi-orientation, curved, noisy and blurry text patches in equipment nameplates. To address this problem, we propose a deep model for text recognition in multi-oriented nameplates, namely, Orientation Robust Scene Text Recognition (ORSTR). Specifically, our model employs a rectification module to transform curved, distorted or multi-orientation text to near-horizontal text with a carefully designed rectification module. Once the near-horizontal text has been generated, recognition network will output the predictions of text patches. Our scene text recognition model achieves 90.8% recognition accuracy on equipment nameplate dataset which outperforms previous scene text recognition model (CRNN) about 0.8%. Several extensive experiments have been conducted to verify the effectiveness of our model. © 2019 IEEE.

关键词： Nameplates

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Modeling 2D image data by robust M-estimation

Modeling 2D image data by robust M-estimation

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Mediterranean Electrotechnical Conference (MELECON)

作者： V. Filova F. Solina J. Lenarcic Joź Stefan Institute Robotics Laboratory University of Ljubljana Ljubljana Slovenia Computer Vision Laboratory Faculty of EE and CS University of Ljubljana Ljubljana Slovenia

The conventional least squared distance method of fitting a model to a set of data points gives unreliable results when the amount of noise in the input is significant compared with the amount of data correlated to the model itself. The theory of robust statistics formally addresses these problems and is used in this work to develop a method of separation of the data of interest from noise. It is based on iteratively reweighted least squares algorithm where Hampel redescending function is applied for weighting data. The method has been efficiently tested in modeling synthetic and real 2D image data with second order curves.< >

关键词： Noise robustness Laboratories Least squares methods computer errors computer vision Integrated circuit modeling Mathematical model Robots Testing Least squares approximation

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Class-specific grasping of 3D objects from a single 2D image

Class-specific grasping of 3D objects from a single 2D image

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2010 IEEE/RSJ International Conference on Intelligent Robots and Systems

作者： Han-Pang Chiu Huan Liu Leslie Pack Kaelbling Tomás Lozano-Pérez Vision and Robotics Laboratory Sarnoff Corporation Princeton NJ USA Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology Cambridge MA USA

Our goal is to grasp 3D objects given a single image, by using prior 3D shape models of object classes. The shape models, defined as a collection of oriented primitive shapes centered at fixed 3D positions, can be learned from a few labeled images for each class. The 3D class model can then be used to estimate the 3D shape of a detected object, including occluded parts, from a single image. The estimated 3D shape is used as to select one of the target grasps for the object. We show that our 3D shape estimation is sufficiently accurate for a robot to successfully grasp the object, even in situations where the part to be grasped is not visible in the input image.

关键词： Three dimensional displays Solid modeling Shape Image reconstruction Computational modeling Grasping Training

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The mapping of texture on VR polygonal models

The mapping of texture on VR polygonal models

引用

International Conference on 3D Imaging, Modeling, Processing, Visualization and Transmission (3DIMPVT)

作者： D. Laurendeau N. Bertrand R. Houde Department of Electrical Engineering and Computer Engineering Computer Vision and Systems Laboratory Laval University QUE Canada Robotics Division Hydro-Quebec Research Institute (IREQ) Varennes QUE Canada

The paper addresses the problem of choosing automatically, and without user intervention, the best possible 2D image that should be used to map as a texture on polygons of a geometric model of complex scenes made of several objects. The geometric models and the texture data are obtained separately: the models are built from range data while the texture information is obtained from 2D color images of the scene. The system does not make any assumption on the way the texture data has been acquired. Texture selection results are presented for an indoor scene.

关键词： Virtual reality Layout Solid modeling computer vision Color computer graphics Rendering (computer graphics) Cameras Laboratories Data engineering

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Fast Candidate Region Extraction for SAR Ship Target 37

Fast Candidate Region Extraction for SAR Ship Target

引用

37th Youth Academic Annual Conference of Chinese Association of Automation, YAC 2022

作者： Zhang, Panpan Luo, Haibo Xu, Zheng He, Miao Shenyang Institute of Automation Shenyang110016 China Institutes for Robotics and Intelligent Manufacturing Shenyang110016 China University of Chinese Academy of Sciences Beijing100049 China Key Laboratory of Opto-Electronic Information Processing Shenyang110016 China The Key Lab of Image Understanding and Computer Vision Shenyang110016 China

ISBN: (纸本)9781665465366

At present, deep learning technology is widely used in ship target detection in synthetic aperture radar (SAR) images. However, high-resolution remote sensing SAR images cover a larger area and have larger image sizes. To be able to use the deep learning model for training and testing, the image needs to be cropped to the appropriate size. In a high-resolution SAR ship image, the ship target usually takes only a small part of the whole image. As a result, only part of the cropped image contains the target, and most of the rest are background regions. This will cause a lot of computational redundancy in the model inference stage. To solve this problem, a fast candidate region extraction algorithm is proposed in this paper for ship target extraction. The algorithm consists of three parts: firstly, extract the saliency map of the SAR ship image, secondly, perform median filtering on the segmented image, and thirdly, extract candidate regions. The superiority of the algorithm was demonstrated by experiments on the AIR-SARShip-1.0 dataset. © 2022 IEEE.

关键词： Synthetic aperture radar

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The equipment nameplate dataset for scene text detection and recognition

The equipment nameplate dataset for scene text detection and...

引用

2019 IEEE International Conference on robotics and Biomimetics, ROBIO 2019

作者： Chen, Xiaolong Zhang, Zhengfu Qiao, Yu Zhang, Pu Guo, Lanqing Chen, Wenrui Chen, Chen Fu, Bin Guangzhou Power Supply Bureau Co. Ltd. Guangzhou China Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences ShenZhen Key Lab of Computer Vision and Pattern Recognition SIAT-SenseTime Joint Lab China SIAT Branch Shenzhen Institute of Artificial Intelligence and Robotics for Society China

ISBN: (纸本)9781728163215

In this paper, we introduce the Equipment Nameplate Dataset, a large dataset for scene text detection and recognition. Natural images in this dataset are taken in the wild and thus this dataset includes various intra-class inconsistency such as ill illumination conditions and partly occluded, which makes our dataset more challenging than other datasets. In order to make people train detection and recognition model separately, we annotate our dataset not only word instance, but also text region by using rectangle bounding boxes. Some detailed statistics information about our dataset will be given so that people can use them to analyse and develop their own models. Moreover, we use our dataset to test some famous detection and recognition models and present the corresponding results in order to make researcher compare them with their own models. Dataset will be publicly available on the website. © 2019 IEEE.

关键词： computer vision

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