检索结果-内蒙古大学图书馆

IEEE International Conference on robotics and Biomimetics

作者： Meng Liu Jianwei Niu Yu Liu China North Artificial Intelligence and Innovation Research Institute Beijing China China North Vehicle Research Institute Beijing China State Key Laboratory of Software Development Environment School of Computer Science and Engineering Beihang University Beijing China State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang University Beijing China Hangzhou Innovation Institute Beihang University Hangzhou China Zhengzhou University Research Institute of Industrial Technology School of Information Engineering Zhengzhou University Zhengzhou China State Key Laboratory of Software Development Envi-ronment School of Computer Science and Engineering Beihang University Beijing China

ISBN: (纸本)9781665481106

Accurate and efficient 3D object detection in LiDAR point cloud is critical for autonomous driving. In this paper, we provide a solution for LiDAR-only detection which employs voxel features and a sparse convolution network. Specifically, we utilized voxel centers to encode point clouds' voxel features. Then, combining the sparse convolution method, we leveraged residual learning to design the backbone network. The detector output predicted results for multi-category and multi-object detection. Our method was evaluated on three popular and challenging datasets (KITTI, nuScence and Waymo). Experimental results demonstrated that the accuracy and speed (27.8 FPS) of our model could effectively support object detection for autonomous driving in various scenarios.

关键词： Point cloud compression Laser radar Three-dimensional displays Convolution Object detection Detectors Feature extraction

来源：评论

学校读者我要写书评

暂无评论

Counting dense objects in remote sensing images

arXiv

引用

arXiv 2020年

作者： Gao, Guangshuai Liu, Qingjie Wang, Yunhong State Key Laboratory of Virtual Reality Technology and Systems Beihang University Hangzhou Innovation Institute Beihang University Hangzhou China

Estimating accurate number of interested objects from a given image is a challenging yet important task. Significant efforts have been made to address this problem and achieve great progress, yet counting number of ground objects from remote sensing images is barely studied. In this paper, we are interested in counting dense objects from remote sensing images. Compared with object counting in natural scene, this task is challenging in following factors: large scale variation, complex cluttered background and orientation arbitrariness. More importantly, the scarcity of data severely limits the development of research in this field. To address these issues, we first construct a large-scale object counting dataset based on remote sensing images, which contains four kinds of objects: buildings, crowded ships in harbor, large-vehicles and small-vehicles in parking lot. We then benchmark the dataset by designing a novel neural network which can generate density map of an input image. The proposed network consists of three parts namely convolution block attention module (CBAM), scale pyramid module (SPM) and deformable convolution module (DCM). Experiments on the proposed dataset and comparisons with state of the art methods demonstrate the challenging of the proposed dataset, and superiority and effectiveness of our method. Copyright © 2020, The Authors. All rights reserved.

关键词： Convolution

来源：评论

学校读者我要写书评

暂无评论

Bidirectional Optimization Coupled Ligh tweight Net works for Efficient and Robust Multi-Person 2D Pose Estimation

引用

Journal of Computer Science & technology 2019年第3期34卷 522-536页

作者： Shnai Li Zheng Fang Wen-Feng Song Ai-Min Hao Hong Qin State Key Laboratory of Virtual Reality Technology and Systems Beihang UniversityBeijing 100191China Beihang University Qingdao Research Institute Qingdao 266000China Department of Computer Science Stony Brook UniversityStony Brook 11790U.S.A.

For multi-person 2D pose estimation,current deep learning baised methods have exhibited impressive performance,but the trade-offs among efficiency,robustness,and accuracy in the existing approaches remain *** principle,bottom-up methods are superior to top-down methods in efficiency,but they perform worse in *** make full use of their respective advantages,in this paper we design a novel bidirectional optimization coupled lightweight network(BOCLN)architecture for efficient,robust,and general-purpose multi-person 2D(2-dimensional)pose estimation from natural *** the BOCLN framework,the bottom-up network focuses oil global features,while the top-down net work places emphasis on det ailed *** entire framework shares global features along the bottom-up data stream,while the top-down data stream aims to accelerate the accurate pose *** particular,to exploit the priors of human joints'relationship,we propose a probability limb heat map to represent the spatial context of the joints and guide the overall pose skeleton prediction,so that each person's pose estimation in cluttered scenes(involving crowd)could be as accurate and robust as ***,benefiting from the novel BOCLN architecture,the tinie-consuming refinement procedure could be much simplified to an efficient lightweight *** experiments and evaluations on public benchmarks have confirmed that our new method is more efficient and robust,yet still attain competitive accuracy performance compared with the state-of-the-art *** BOCLN shows even greater promise in online applications.

关键词： bidirectional optimization computer vision deep learning probability limb heat map 2D multi-person pose estimation

来源：评论

学校读者我要写书评

暂无评论

Abnormal Data Cleaning for Wind Turbines by Image Segmentation Based on Active Shape Model and Class Uncertainty

SSRN

引用

SSRN 2022年

作者： Liang, Guoyuan Su, Yahao Wu, Xinyu Ma, Jiajun Long, Huan Song, Zhe Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Guangdong Province Shenzhen China Guangdong Provincial Key Laboratory of Computer Vision and Virtual Reality Technology Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Guangdong Province Shenzhen China Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Guangdong Province Shenzhen China School of Electrical Engineering Southeast University Jiang Su Province Nanjing China School of Business Nanjing University Jiang Su Province Nanjing China

Wind power curve describes the relationship between wind speed and output power of wind turbine, which may be contaminated due to various unexpected factors. Following the idea of image segmentation in our previous work, in this paper, we propose a novel image-based abnormal data cleaning algorithm aiming to provide high quality data for wind power curve modeling and relevant applications. For image binarization, a multiscale space is built to determine the optimal scale at which the most stable as well as informative shape representations are extracted. The Active Shape Model is utilized to model the power curve shape and generate a set of segmentation candidates for filtering abnormal data. To measure the cost of each candidate, the Class Uncertainty map based on dual feature images is built. And eventually the optimal segmentation boundary is determined by the optimization in the energy space. A series of comparative experiments conducted over the real-world datasets of 37 wind turbines have validated the superior performance of the proposed algorithm. © 2022, The Authors. All rights reserved.

关键词： Wind power

来源：评论

学校读者我要写书评

暂无评论

A Novel Shape-based Robotic Sorting Approach based on Computer Vision

A Novel Shape-based Robotic Sorting Approach based on Comput...

引用

IEEE International Conference on Big Data and Cloud Computing (BdCloud)

作者： Tanhong Chen Tao Ren Jianwei Niu Qingfeng Li State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang University Beijing China Hangzhou Innovation Institute Beihang University Hangzhou China Zhengzhou University Research Institute of Industrial Technology School of Information Engineering Zhengzhou University Zhengzhou China

In recent years, intelligent robotic sorting has be-come a popular application of industrial robots, whose performance is to a large extent affected by object estimation. Many researchers have been devoted to design efficient pose estimation methods based on deep learning. However, there remains two challenges. One is that the annotation of the dataset is labor-intensive and time-consuming, which makes it difficult to build large pose estimation datasets. The other is that, for objects of interest, the final pose estimation depends on an accurate 3D model of the object, which makes most existing methods of object pose estimation stay at the instance level, i.e., only objects known to the method can be identified. To address the above challenges, this paper employs the game engine to build a virtual dataset that can be automatically annotated, and proposes a shape-based robot vision sorting approach that can efficiently classify and grasp objects with regular shapes. Experimental results indicate that the proposed approach can achieve category-level object pose estimation and thus make robot grasping more applicable.

关键词： Solid modeling Three-dimensional displays Service robots Shape Pose estimation Object segmentation Grasping

来源：评论

学校读者我要写书评

暂无评论

Object Pose Estimation for Robotic Grasping based on Multi-view keypoint Detection

Object Pose Estimation for Robotic Grasping based on Multi-v...

引用

IEEE International Conference on Big Data and Cloud Computing (BdCloud)

作者： Zheyuan Hu Renluan Hou Jianwei Niu Xiaolong Yu Tao Ren Qingfeng Li State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang University Beijing China Hangzhou Innovation Institute Beihang University Hangzhou China Zhengzhou University Research Institute of Industrial Technology School of Information Engineering Zhengzhou University Zhengzhou China

Industrial robots can replace human labour to perform a variety of tasks. Among these tasks, robotic grasping is the most primary industrial robot operation. However, conventional robotic grasping methods could become inapplicable for cluttered and occluded objects. To address the issue, we adopt object pose estimation (OPE) to facilitate robotic grasping of cluttered and occluded objects and propose an object detection model based on 2D-RGB multi-view features. The proposed model is built by adding four transpose convolution layers into the Resnet backbone to obtain desirable 2D feature maps of object keypoints in each image. In addition, we design a feature-fusion model to produce 3D coordinates of keypoints from 2D multi-view features based on the volumetric aggregation method, along with a keypoint-detection confidence of each view to assist the optimality judgment of the robotic grasping. Extensive experiments are conducted to verify the accuracy of OPE, and the experimental results indicate the substantial performance improvements of the proposed approach over conventional methods in various scenarios.

关键词： Solid modeling Three-dimensional displays Service robots Image color analysis Robot kinematics Pose estimation Robot vision systems

来源：评论

学校读者我要写书评

暂无评论

Scenario analysis on abating industrial process greenhouse gas emissions from adipic acid production in China

引用

Petroleum Science 2020年第4期17卷 1171-1179页

作者： Qing Tong Han-Yi Lin Xu-Ying Qin Run-Sheng Yan Yue-Feng Guo Xin-Yang Wei Institute of Nuclear and New Energy Technology Tsinghua UniversityBeijing 100084China School of Energy Power and Mechanical EngineeringNorth China Electric Power UniversityBeijing 102206China Environmental Protection Research Institute of Light Industry Beijing 100089China State Key Laboratory of Virtual Reality and Suzhou Institute of Beihang University Suzhou 215200China Department of Accounting and Finance School of BusinessMacao University of Science and TechnologyMacao 999078China

Adipic acid is an important petrochemical product,and its production process emits a high concentration of greenhouse gas N_2 *** paper aims to provide quantitative references for relevant authorities to formulate greenhouse gas control *** forecasting method of this paper is consistent with the published national inventory in terms of *** on the N_2 O abatement technical parameters of adipic acid and the production trend,this paper combines the scenario analysis and provides a measurement of comprehensive N_2 O abatement effect of the entire industry in *** future scenarios are *** baseline scenario(BAUS) is a frozen *** emission abatement scenarios(ANAS,SNAS,and ENAS) are assumed under different strength of abatement driving *** results show that China's adipic acid production process can achieve increasingly significant N_2 O emission abatement *** to the baseline scenario,by 2030,the N_2 O emission abatements of the three emission abatement scenarios can reach 207-399 kt and the emission abatement ratios can reach 32.5%-62.6%.By 2050,the N_2 O emission abatements for the three emission abatement scenarios can reach 387-540 kt and the emission abatement ratios can reach 71.4%-99.6%.

关键词： Scenario analysis Industrial process greenhouse gas emissions Adipic acid N2O emission abatement China

来源：评论

学校读者我要写书评

暂无评论

Deep Depth Completion from Extremely Sparse Data: A Survey

arXiv

引用

arXiv 2022年

作者： Hu, Junjie Bao, Chenyu Ozay, Mete Fan, Chenyou Gao, Qing Liu, Honghai Lam, Tin Lun China Samsung Research United Kingdom School of Artificial Intelligence South China Normal University China State Key Laboratory of Robotics and Systems Harbin Institute of Technology Shenzhen China Chinese University of Hong Kong Shenzhen China

Depth completion aims at predicting dense pixel-wise depth from an extremely sparse map captured from a depth sensor, e.g., LiDARs. It plays an essential role in various applications such as autonomous driving, 3D reconstruction, augmented reality, and robot navigation. Recent successes on the task have been demonstrated and dominated by deep learning based solutions. In this article, for the first time, we provide a comprehensive literature review that helps readers better grasp the research trends and clearly understand the current advances. We investigate the related studies from the design aspects of network architectures, loss functions, benchmark datasets, and learning strategies with a proposal of a novel taxonomy that categorizes existing methods. Besides, we present a quantitative comparison of model performance on three widely used benchmarks, including indoor and outdoor datasets. Finally, we discuss the challenges of prior works and provide readers with some insights for future research directions. © 2022, CC BY.

关键词： Augmented reality

来源：评论

学校读者我要写书评

暂无评论

An open-source project for real-time image semantic segmentation

引用

Science China(Information Sciences) 2019年第12期62卷 246-247页

作者： Quan ZHOU Yu WANG Jia LIU Xin JIN Longin Jan LATECKI National Engineering Research Center of Communication and Network Technology Nanjing University of Posts and Telecommunications Key Laboratory of Ministry of Education for Broadband Wireless Communication and Sensor Network Technology Nanjing University of Posts and Telecommunications Beijing Electronic Science and Technology Institute State Key Laboratory of Virtual Reality Technology and Systems Beihang University Department of Computer and Information Sciences Temple University

Recent years have witnessed great progress of deep convolutional neural networks (DCNNs) for solving scene understanding tasks [1–3]. These advances prefer to construct deeper and larger net-

关键词： image An open-source project for real-time image semantic segmentation

来源：评论

学校读者我要写书评

暂无评论

Parallel Large-Scale Structure from Motion by Distributed Averaging 4

Parallel Large-Scale Structure from Motion by Distributed Av...

引用

4th International Conference on Electronic Information technology and Computer Engineering, EITCE 2020

作者： Lin, Pengfei Bao, Yongtang Du, Wenxiang Qi, Yue Beihang University Beihang University Qingdao Research Institute Qingdao China College of Computer Science and Engineering Shandong University of Science and Technology Qingdao China State Key Laboratory of Virtual Reality and Technology Beihang University Beihang University Qingdao Research Institute Beijing China

ISBN: (纸本)9781450387811

With the development of computer vision, Structure from Motion (SFM) which recovers sparse point clouds from image sequences has achieved great success. Large-scale scenes cannot be reconstructed with a single compute node so that we introduce a divide-And-conquer framework to solve the distributed SFM problem. First, we attach great importance to the efficiency of image matching and geometric filtering, which takes up a lot of time in the traditional SFM problem. We use the GPS information of images to calculate the GPS neighborhood. The number of image matches is greatly reduced by matching each image with only valid GPS neighbors, and a robust matching relationship is obtained. Second, the calculated matching relationship is used as the initial camera graph to be divided into multiple subgraphs by the clustering algorithm, and local SFM is executed on several computing nodes to register the local cameras. Finally, all local camera poses are integrated and optimized to complete the global camera registration. Our system can solve the structure from motion problem in large-scale scenes with accuracy and efficiency. © 2020 ACM.

关键词： Global positioning system

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：