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检索条件"任意字段=18th International Conference on Control Automation Robotics and Vision"

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Fish Keypoints Detection for Ecology Monitoring Based on Underwater Visual Intelligence

Fish Keypoints Detection for Ecology Monitoring Based on Und...

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international conference on control, automation, robotics and vision (ICARCV)

作者： Feiyang Suo Kangwei Huang Gui Ling Yanjun Li Ji Xiang College of Control Science and Engineering Zhejiang University Hangzhou China School of Information and Electrical Engineering Zhejiang University City College Hangzhou China College of Electrical Engineering Zhejiang University Hangzhou China

ISBN: (数字)9781728177090

ISBN: (纸本)9781728177106

this paper introduces a fishery ecology monitoring system for cultivation pools, and proposes a new stereo keypoint detection method followed by curve fitting analysis to estimate the fish posture and length. the system, which can be employed for aquaculture monitoring, is featured by its exploitation of underwater visual intelligence and deep neural-network architecture. As input, stereo image pairs are obtained by underwater binocular camera. A deep neural-network under Faster R-CNN architecture is built to detect fish from the stereo image inputs. Another network under Stacked Hourglass architecture is constructed to detect specific keypoints of each fish. For ecology monitoring, detected keypoints are used in the estimation of the fishes' posture and length. Unlike other size estimation methods which also apply a binocular camera, our method naturally bypasses the pixel-wise matching difficulty in global stereo matching algorithms. Experiment shows that our system is applicable for online fish ecology monitoring, with efficient and accurate estimation performance.

关键词： Monitoring Ecology Cameras Estimation Visualization Heating systems Fish

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Extracting fetal heart rate from abdominal ECGs based on fast multivariate empirical mode decomposition

Extracting fetal heart rate from abdominal ECGs based on fas...

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international conference on control, automation, robotics and vision (ICARCV)

作者： Jiayue Zhang Xiaozhou Xu Qiming Chen Lei Xie Hongye Su State Key Laboratory of Industrial Control Technology Zhejiang University Hangzhou China

ISBN: (数字)9781728177090

ISBN: (纸本)9781728177106

Abdominal electrocardiogram is an important means to obtain fetal health condition during high-risk pregnancy. In this paper, a novel method for extracting fetal heart rate from multi-channel mother abdomen electrocardiograms is proposed using fast multivariate empirical mode decomposition technique (FMEMD). Firstly, FMEMD decomposes the multichannel ECG signals into a set of modes. Two significant channels are selected according to the standard deviation of the fifth layer. then the continuous wavelet transform technique (CWT) is applied to these two channels to denoise. the baseline is removed by zero-crossing rate. Following, the interference of the mother QRS complexes and non-overlapped fetal R-peaks can be eliminated and detected by CWT coefficient. the overlapped fetal R-peaks are obtained by combining the dynamic pattern matching program and creative algorithm. the proposed method achieves an accuracy of 99.9% on the existing data set, and the calculating time is only 1/6.39 of the MEMD-based method.

关键词： Standards Continuous wavelet transforms Electrocardiography Pattern matching Monitoring Heuristic algorithms Fetal heart rate

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Analytical Change Detection on the KITTI dataset

Analytical Change Detection on the KITTI dataset

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international conference on control, automation, robotics and vision (ICARCV)

作者： Johannes Schauer Marin Rodrigues Andreas Nüchter Julius-Maximilians-University Wurzburg Germany Robotics and Telematics Julius-Maximilians-University Würzburg Germany

ISBN: (数字)9781728177090

ISBN: (纸本)9781728177106

We present an algorithm for explicit change detection on 3D point cloud data from a mobile mapping scenario, namely the KITTI dataset. Our method is able to partition a 3D point cloud into static and dynamic points using ray traversal of a 3D voxel grid. We are thus not using a machine learning approach or RGB camera data but instead compute the intersections of the scene volume with the lines-of-sight between the sensor and the measured points. Our approach does thus not require any object detection or tracking and has comparatively low requirements on the hardware. While our earlier work focused on dense point clouds from terrestrial 3D laser scans, here we investigate its application on the sparse 3D point clouds produced by a Velodyne laser range finder in a mobile mapping scenario and compare our results to two competing implementations using the ground truth annotation from FuseMODNet for a quantitative analysis. We also introduce spherical quadtree point cloud reduction as a way to only work on less than 1% of the original data, making processing multiple times faster while at the same time producing results with equivalent F 1 scores.

关键词： three-dimensional displays Cameras Benchmark testing Volume measurement Heuristic algorithms Geometry Data structures

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Human-computer interaction control of snake-like robot based on gesture recognition 2019

Human-computer interaction control of snake-like robot based...

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4th international conference on automation, control and robotics Engineering, CACRE 2019

作者： Chen, Yidong Wei, Wu Xiao, Wenyu Automation Department South China University of Technology Guangzhou China

ISBN: (纸本)9781450371865

Human-computer interaction has been a hot topic in the development of robots. Gesture recognition is a significant part of it. Existing snake-like robot is controlled by the handle and joystick. It is worth studying that use gesture to control robots. Existing main vision-based gesture recognition methods are based on a two-stage method: hand detection and segmentation, feature extraction and template matching process. Inspired by the YOLO[1] algorithm, this paper proposed a one-stage gesture recognition and snake-like robot control method, based on deep learning, combined image segmentation and classification, realized end-to-end recognition and robot control. the snake-like robot can react to four types of human’s gestures with a rapid processing time. © 2019 Association for Computing Machinery.

关键词： Human computer interaction

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Quantifying Tissue-Stiffening Behaviour of the cervix in Shear-wave Elastography

Quantifying Tissue-Stiffening Behaviour of the cervix in She...

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international conference on control, automation, robotics and vision (ICARCV)

作者： Weirong Gel Graham Brooker Jon Hyett Central Clinical School University of Sydney Australian Centre for Field Robotics

ISBN: (数字)9781728177090

ISBN: (纸本)9781728177106

Shear-wave elastography is a useful tool in imaging mechanical properties of soft-tissue. It has great potential to improve the predictive accuracy of preterm birth. this imaging modality, however, has high inter- and intra-observer variability. this is due its sensitivity to changes in pressure and the natural variation in elasticity across the cervix. the aim of this study is to use the artefacts caused by the pressure-dependent increase in shear-wave speed to derive fourth order hyperelastic elasticity tensor of the tissue. the method proposed is as follows: the strain-map of the tissue is extracted by tracking the deformation of the cervix in two raw B-mode images of the ultrasound. Each B-mode image is correlated to a shear-wave map acquired at the same pressure. the relation of the shear-wave speed to the strain is used to derive the parameters of the hyperelastic material model of cervical tissue. A continuous measure of the cervical elasticity parameters is taken. the maps of the parameters are then used to predict the delivery date. In this paper, this method is demonstrated through simulation in FEBio in preparation for a large-scale clinical study at the Royal Prince Alfred Hospital. In the simulation, a tissue of Mooney Rivilin hyperelastic material is indented from 0 mm to 6 mm using a model of the ultrasound probe. the maximum factor increase in shear modulus was 2.63 times which translates to 62 % increase in measured shear-wave speed. the function of the change in shear-wave speed is unique to the parameters of the model.

关键词： Elasticity Strain Probes Elastography Solid modeling Deformable models Tensors

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A Compliance control Strategy for Adapting to Body Movements during a Percutaneous Surgery 9

A Compliance Control Strategy for Adapting to Body Movements...

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9th IEEE international conference on Cybernetics and Intelligent Systems (CIS) / IEEE conference on robotics, automation and Mechatronics (RAM)

作者： Fang, Chenhao Wan, Ziwei Qiu, Yangfeng Zhou, Chunlin Zhejiang Univ Coll Control Sci & Engn Hangzhou Peoples R China Zhejiang Univ Statc Key Lab Ind Control Technol Hangzhou Peoples R China

ISBN: (纸本)9781728134581

Currently, robots have been used in percutaneous puncture surgery to solve the problems of low precision and heavy workload. However, there is a problem in robotic-assisted puncture surgery that the operative region may move relative to the rigid robot, especially in lung surgery, which may aggravate the patient's trauma. thus, in this paper, we design a compliance control strategy for adapting to body movements. Taking advantage of a six-dimensional force sensor and the gravity compensation strategy, the compliance control strategy based on impedance control can control the contact force between the patient and the puncture needle to a small range. To cooperate with this control strategy, we also develop a percutaneous puncture surgery robot with high positioning accuracy, ample working space, and good operationality. the effectiveness of this strategy was validated in a simplified scenario of lung puncture surgery, where the puncture needle could track the movement of the lung organ model with relative displacement less than 2mm, the contact force less than 1.2N, the contact torque less than 0.3N*m.

关键词： percutaneous surgery gravity compensation strategy impedance control six-dimensional force sensor

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A Latent Variables Augmentation Method Based on Adversarial Training for Image Categorization with Insufficient Training Samples

A Latent Variables Augmentation Method Based on Adversarial ...

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international conference on control, automation, robotics and vision (ICARCV)

作者： Luyue Lin Dacai Liu Bo Liu Yanshan Xiao School of Automation Guangdong University of Technology China School of Computers Guangdong University of Technology China

ISBN: (数字)9781728177090

ISBN: (纸本)9781728177106

In the past few years, people have made great progress in image categorization based on convolutional neural networks (CNN) with the large-scale training set. However, in real-world applications, we may only have insufficient training samples for training CNN. To solve this problem, in this paper, we propose a latent variable augmentation method based on adversarial training (Lagat) to increase the categorization accuracy of CNN with insufficient training samples. In the proposed Lagat model, we propose a uniform loss function, where we take the following two tasks into account. Firstly, we consider the task that we augment latent variables (LVs) from a set of class-specific adaptive distributions and we propose constraints to adjust these LVs distributions. Secondly, we use these sampled LVs to train a predictive image classifier. Moreover, we present an alternative two-play minimization game to optimize this uniform loss function. In addition, the experiment results also demonstrate that the proposed Lagat method delivers higher accuracy than the existing state-of-the-art methods. the source code of the Lagat algorithm is available in Github 11 https://***/itapty-ily/Lagat.

关键词： Training Generative adversarial networks Feature extraction Gallium nitride Task analysis Gaussian distribution Covariance matrices

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Hybrid DDPG Approach for Vehicle Motion Planning 16

Hybrid DDPG Approach for Vehicle Motion Planning

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16th international conference on Informatics in control, automation and robotics (ICINCO)

作者： Feher, Arpad Aradi, Szilard Hegedus, Ferenc Becsi, Tamas Gaspar, Peter Budapest Univ Technol & Econ Dept Control Transportat & Vehicle Syst Budapest Hungary Hungarian Acad Sci Comp & Automat Res Inst Budapest Hungary

ISBN: (纸本)9789897583803

the paper presents a motion planning solution which combines classic control techniques with machine learning. For this task, a reinforcement learning environment has been created, where the quality of the fulfilment of the designed path by a classic control loop provides the reward function. System dynamics is described by a nonlinear planar single track vehicle model with dynamic wheel mode model. the goodness of the planned trajectory is evaluated by driving the vehicle along the track. the paper shows that this encapsulated problem and environment provides a one-step reinforcement learning task with continuous actions that can be handled with Deep Deterministic Policy Gradient learning agent. the solution of the problem provides a real-time neural network-based motion planner along with a tracking algorithm, and since the trained network provides a preliminary estimate on the expected reward of the current state-action pair, the system acts as a trajectory feasibility estimator as well.

关键词： Reinforcement Learning Motion Planning Autonomous Vehicles Automotive control

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Detection of Obstacle Features Using Neural Networks with Attention in the Task of Autonomous Navigation of Mobile Robots 20th

Detection of Obstacle Features Using Neural Networks with At...

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20th international conference on Computational Science and Its Applications (ICCSA)

作者： Sviatov, Kirill Miheev, Alexander Sukhov, Sergey Lapshov, Yuriy Rapp, Stefan Ulyanovsk State Tech Univ Ulyanovsk Russia VA Kotelnikov Russian Acad Sci Inst Radio Engn & Elect Ulyanovsk Branch Moscow Russia Darmstadt Univ Appl Sci Darmstadt Germany

ISBN: (纸本)9783030588175;9783030588168

this article describes the design process of a software package for image recognition of a mobile robot camera using neural networks with attention, which allows to identify the probability of a robot colliding with obstacles standing in its way. A key feature of this software is using a dataset that is prepared without manual labeling of all obstacles and the probability of a collision. Currently, an important task in mobile robotics is the need to use numerous heuristics and deterministic algorithms in control programs along with neural networks. the use of a single neural network that solves all the tasks of scene analysis (the so-called "end-to-end" solution) is impossible for several reasons: the high complexity of the training samples due to the large parameter space of the environment of the robot and the insufficient formalization of these parameters, as well as the computational complexity of machine learning algorithms, which is critical for mobile robots with strict energy requirements. therefore, the development of a universal algorithm (end-to-end) is a laborious process. the article describes a method that allows to use weakly formalized parameters of the robot environment for training convolutional neural networks with attention using the obstacle recognition task. At the same time, weak formalization reduces the time-consuming process of manual data labeling due to automatically generated datasets in the NVIDIA Isaac environment, and the attention mechanism allows increasing the interpretability of the analysis results.

关键词： Artificial intelligence Neural networks Machine learning Computer vision Attention networks Self-driving car

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A New Robot Body Calibration Method of SCARA Based on Machine vision

A New Robot Body Calibration Method of SCARA Based on Machin...

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2019 IEEE international conference on robotics and Biomimetics, ROBIO 2019

作者： Zhang, Xiuan Xie, Xiaohui Soochow University Department of Mechanical and Electrical Engineering SuZhou Jiangsu Province China

ISBN: (纸本)9781728163215

With the development of industrial automation, SCARA robot is widely used in industrial production because of its flexible action and fast speed. there is a certain error between the theoretical model and the actual model of SCARA robot due to the inconsistent in production and assembly process, which makes the robot positioning accuracy not guaranteed. At the same time, although the traditional calibration method like using laser tracker has high calibration accuracy, but the equipment is expensive and the calibration time is too long. In this paper, a calibration method based on machine vision for the SCARA robot is proposed, which uses the position error model to ensure the positioning accuracy of the robot's left-hand and right-hand systems. the calibration software is easy to use and the process is almost in automation. the experimental results show that the calibrating time is shorter and the positioning accuracy can be guaranteed. © 2019 IEEE.

关键词： Computer vision

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