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Autonomous Obstacle Avoidance Scheme Using Monocular Vision Applied to Mobile Robots

Autonomous Obstacle Avoidance Scheme Using Monocular Vision ...

2020 International Conference on intelligent Control, Measurement and Signal Processing and intelligent Oil Field, ICMSP 2020

作者： Song, Yun Yun Yang, Sheng Yi Liu, Chao Wu, Qing Gui School of Data Science and Information Engineering Key Laboratory of Pattern Recognition and Intelligent Systems of Guizhou Province Guizhou Minzu University Guiyang China School of Mechatronics Engineering Guizhou Minzu University Guiyang China

Mobile robots cannot move in an unknown environment with static or slow-moving obstacles effectively. We present an enhanced obstacle avoidance strategy using monocular vision to solve this problem. First, we combine Canny and Otsu to extract the barrier feature and find the critical pixel position of obstacles by the monocular vision. Then the image depth estimation algorithm is used to estimate the gaps. With these parameters of barriers, an improved bug algorithm is proposed to avoid the obstacles autonomously. The experiments show that the proposed obstacles avoidance strategy can effectively make a small mobile robot avoid different kinds of obstacles. © Published under licence by IOP Publishing Ltd.

关键词： Mobile robots

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Ballot character recognition based on image processing 2

Ballot character recognition based on image processing

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2nd International Conference on Computer Vision and Data Mining, ICVDM 2021

作者： Zhao, Yu-Qing Yan, Xiao-Bo Wang, Lin College of Date Science and Information Engineering Guizhou Minzu University Guizhou Guiyang550025 China Key Laboratory of Pattern Recognition and Intelligent Systems of Guizhou Guizhou Guiyang550025 China

According to the relevant provisions of the "Election Law of the People's Republic of China", the engineering software required for the implementation of large-scale conference elections can set voting regulations for "differential elections", "equal elections", and "alternative elections". This paper proposes an automatic ballot recognition system based on image processing, which is suitable for all levels of people’s congresses, party and government meetings or other meetings. It has complete functions and stable recognition. It can automatically count and summarize the voting results and display the voting results on the big screen. Show on the display system. Firstly, the paper ballot is scanned by a scanner to obtain the image of the ballot. Secondly, the system will preprocess the image of the ballot and segment the image into ***, according to the handwritten character recognition algorithm based on image processing and Shen filter proposed in this paper, the handwritten characters "√" and "×" in the subregion are *** final experimental results show that the recognition accuracy of the system is as high as 99.82%, which has an absolute advantage over the traditional manual voting, and can meet the performance requirements of high efficiency, accuracy, objectivity and security of large-scale conference election. © 2021 Institute of Physics Publishing. All rights reserved.

关键词： Image segmentation

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Face recognition with Partial Occlusion Based on Attention Mechanism

Face Recognition with Partial Occlusion Based on Attention M...

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2021 International Conference on Electronic Information Engineering and Computer Science, EIECS 2021

作者： Zhao, Yuqing Wang, Lin Tan, Mian Yan, Xiaobo Zhang, Xuewen Feng, Hongxi Guizhou Minzu University College of Date Science and Information Engineering Guiyang China Guizhou Minzu University Key Laboratory of Pattern Recognition and Intelligent Systems of Guizhou Guiyang China

ISBN: (纸本)9781665416740

Face recognition is widely used in our life, face recognition with partial occlusion is of great significance to the improvement of face recognition accuracy in practical application scenarios. For improve the accuracy of face recognition in partial occlusion scene, we propose a MobilenetV1-CBAM-Facenet (MC-Facenet) model. which uses Multi-Task Cascaded Convolutional Neural Networks to complete the detection of partial occlusion, then introduces MobilenetV1 and Attention Mechanism to solve the face recognition with partial occlusion. We designs a new Loss function which combines the Triplet Loss and cross-entropy Loss to increase the convergence rate. We carried out comparative experiments based on the public data set LFW, and demonstrated the effectiveness of MC-Facenet under different occlusion types and different occlusion areas. The experimental results show that the occlusion type is glasses and the occlusion area is 30%, the recognition accuracy is improved by 10.92% and 9.39% respectively compared with the original model. It is proved that MC-Facenet can greatly improve the accuracy of local occlusion face recognition. © 2021 IEEE.

关键词： Face recognition

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Mixture Loss Function-based Classification Network for Few-shot Learning

Mixture Loss Function-based Classification Network for Few-s...

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Computing, Robotics and System Sciences (ICRSS), International Conference on

作者： Yansha Zhang Feng Pan Jie Wang Lin Wang College of Date Science and Information Engineering GuiZhou Minzu University Guiyang China Key Laboratory of Pattern Recognition and Intelligent System of Guizhou Province Guiyang China

Data augmentation technology can effectively solve the problem of few-shot image classification, but many approaches based on data augmentation generated sample have poor discriminability, which negatively affects the classifier and classification accuracy needs to be improved. In this regard, based on the TFFCN, a mixture loss function-based classification network for few-shot learning (MLFCN) is proposed. First of all, for the prototype tensor features with a simple mean representation, ignoring the different contributions of the sample features of support set for per class to the class prototype tensor features, so that the generated prototype tensor features can not well represent sample features of per class, affecting the discriminability of the generated samples. Introduce the improved adaptively spatial feature fusion module, it uses the convolution, considers the support set of different sample features on the prototype tensor, adaptively assign weights, then weighted and summed to obtain a more representative prototype tensor feature; secondly, aiming at the blurred classification boundary of similar categories in the generated sample features, a mixture loss function is proposed. The loss function is to add the inter-class mean squared error term to the mean square error loss, and stretches the inter-class distance of the generated tensor feature, so that the classification boundary is clearer, so as to train a better classifier. Experimental results show that the improved adaptively spatial feature fusion and the mixing loss function can augment the discriminability of the generated tensor features and further improve the classification accuracy.

关键词： Training Tensors Convolution Prototypes Mean square error methods Feature extraction Generators

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Tight reservoirs classification using random forest: A case study of he 8 member in eastern yan'an gas field 3

Tight reservoirs classification using random forest: A case ...

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3rd International Conference on intelligent Control, Measurement and Signal Processing and intelligent Oil Field, ICMSP 2021

作者： Yan, Wang Ruogu, Wang Shengyi, Yang Jianping, Liu College of Petroleum Engineering Xi'An Shiyou University Xi'an China Co. Ltd. Xi'an China Guizhou Minzu University Key Laboratory of Pattern Recognition and Intelligent Systems of Guizhou Province Guiyang China Changqing Oilfield Company Exploration Department of PetroChina Xi'an China

ISBN: (纸本)9781665437158

Tight sandstone reservoir is very important in oil and gas exploration in China. Tight reservoirs classification and evaluation are a frontier research field. There are many indexes involved in reservoirs classification, and it is necessary to judge the reservoir type according to personal experience, which consumes lots of time and manpower. Therefore, a new classification method of tight reservoirs using random forest is proposed. Firstly, the high pressure mercury injection curves of tight sandstone reservoirs of He 8 member of Lower Shihezi Formation in eastern Yan'an Gas Field are selected as the research data. Four characteristics for classification are obtained by principal component analysis. Secondly, the random forest using CART is used to classify and obtain the results of reservoir classification. Finally, classification results are verified and parameters of the random forest are optimized. Experimental results show that the proposed reservoirs classification method has high accuracy and low calculation cost. It can effectively reduce time loss and save manpower, and has good generalization. © 2021 IEEE.

关键词： Principal component analysis

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Geometric Numerical Integral Method in Compact Lie Group

Geometric Numerical Integral Method in Compact Lie Group

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6th International Conference on intelligent Computing and Signal Processing (ICSP)

作者： Chao Liu Shengyi Yang Key Laboratory of Pattern Recognition and Intelligent System of Guizhou Province Guizhou Minzu University Gui Yang China

ISBN: (纸本)9781665447300

Three dimensions special orthogonal group SO (3) is widely used to describe the rotation kinematics of the rigid body without local coordinates, which can avoid rotation singularity and unwinding in traditional methods. Propagating the rotation kinematics in SO (3) with a specific geometric integration method is not only to obtained numerical results with improved qualitative behavior, but also provided more accurate long-time integration results. While many studies have focused on geometric integration algorithms to preserve the geometric structure, this work has the additional objective of studying result accuracy. Integral curves on SO (3) obtained using the third-order Crouch-Grossman Lie group method are compared with numerical results using the third-order RKMK algorithms, the exponential coordinates method, and the simple projection method. Results show that the use of the Crouch-Grossman Lie group method better preserves the geometric structure of SO (3) for the larger time steps considered. It is also found that the third-order Crouch-Grossman algorithm is more accurate than the RKMK except for the smallest time step used.

关键词： Heuristic algorithms Signal processing algorithms Kinematics Signal processing

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Gait Planning and Motion Control Based on Vrep Simulation for Quadruped Robot

Gait Planning and Motion Control Based on Vrep Simulation fo...

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WRC Symposium on Advanced Robotics and Automation (WRC SARA)

作者： Linqi Zhou Zhihua Chen Jun Liu Zhi Liu Yumeng Chen Liting Zhang key Laboratory of Jiangxi Province for Image Processing and Pattern Recognition and MOE Key Lab of Nondestructive Testing Technology Nanchang Hangkong University Nanchang China State Key Laboratory of Intelligent Control and Decision of Complex Systems School of Automation Beijing Institute of Technology Beijing China

Gait planning of quadruped robots plays an important role in achieving less walking, including dynamic and static gait. In this article, a static and dynamic gait control method based on center of gravity stability margin is proposed. Firstly, the robot model and kinematics modeling are introduced. Secondly, the robot’s foot static and dynamic gait were planned and the foot trajectory was designed. Finally, two types of gait of the robot were simulated using Vrep simulation software, and the differences in stability and speed between the coordinated gait with speed and stability in the static and dynamic gait of a 12 degree of freedom robot were analyzed, verifying the effectiveness of the gait control method proposed in this paper.

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An Active Landing Recovery Method for Quadrotor UAV: Localization, Tracking and Buffering Landing

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IFAC-PapersOnLine 2023年第2期56卷 3366-3372页

作者： Yongkang Xu Zhihua Chen Shoukun Wang Junzheng Wang National Key Lab of Autonomous Intelligent Unmanned Systems Beijing Institute of Technology Beijing CO 100081 P.R.China Key Laboratory of Jiangxi Province for Image Processing and Pattern Recognition and MOE Key Lab of Nondestructive Testing Technology School of Information Engineering Nanchang Hangkong University Nanchang CO 330063 P.R. China

This paper proposes a principle of fully autonomous ground mobile landing recovery of Unmanned Aerial Vehicles (UAV) for the problems of relatively fixed landing point, passive recovery, poor flexibility, and environmental adaptability, which mainly includes localization, landing point tracking, and buffering landing for quadrotor UAV. Firstly, aiming at the problem that it is difficult to accurately obtain the position of a UAV in dynamic mobile landing recovery, a target location method based on Asynchronous Multisensor Information Fusion(AMIF) and servo turntable focus tracking is proposed. Secondly, to achieve fast and high-precision tracking of UAVs, a tracking control strategy of an independently driven landing recovery system and a Stewart six-degree of freedom platform is proposed. Then, to solve the problems of large impact force and center of gravity instability in the landing process of UAV, a stationarity control algorithm based on model prediction and a compliance control algorithm based on adaptive variable impedance are designed to achieve active compliance control while adjusting the position and attitude of the receiving surface in real-time. Finally, a quadrotor unmanned landing and recovery experimental platform is built to verify the feasibility of the ground mobile landing and recovery strategy proposed in this paper and the effectiveness of the control algorithm.

关键词： Quadrotor UAV Mobile Autonomous recovery Target localization Falling point tracking Buffering landing

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Autonomous Obstacle Avoidance Scheme Using Monocular Vision Applied to Mobile Robots

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Journal of Physics: Conference Series 2021年第1期1894卷

作者： Yun Yun Song Sheng Yi Yang Chao Liu Qing Gui Wu School of Data Science and Information Engineering Key Laboratory of Pattern Recognition and Intelligent Systems of Guizhou Province Guizhou Minzu University Guiyang China Key Laboratory of Pattern Recognition and Intelligent Systems of Guizhou Province Guizhou Minzu University Guiyang China School of Mechatronics Engineering Guizhou Minzu University Guiyang China

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Pulsar candidate selection using ensemble networks for FAST drift-scan survey

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Science China(Physics,Mechanics & Astronomy) 2019年第5期62卷 61-70页

作者： HongFeng Wang WeiWei Zhu Ping Guo Di Li SiBo Feng Qian Yin ChenChen Miao ZhenZhao Tao ZhiChen Pan Pei Wang Xin Zheng XiaoDan Deng ZhiJie Liu XiaoYao Xie XuHong Yu ShanPing You Hui Zhang FAST Collaboration Image Processing and Pattern Recognition Laboratory College of Information Science and TechnologyBeijing Normal UniversityBeijing 100875China CAS Key Laboratory of FAST Chinese Academy of SciencesBeijing 100101China School of Information Management Dezhou UniversityDezhou 253023China Image Processing and Pattern Recognition Laboratory School of Systems ScienceBeijing Normal UniversityBeijing 100875China University of Chinese Academy of Sciences Beijing 100049China Key Laboratory of Information and Computing Science Guizhou Province Guizhou Normal UniversityGuiyang 550001China School of Physics and Electronic Science Guizhou Normal UniversityGuiyang 550001China 不详

The Commensal Radio Astronomy Five-hundred-meter Aperture Spherical radio Telescope(FAST) Survey(CRAFTS) utilizes the novel drift-scan commensal survey mode of FAST and can generate billions of pulsar candidate signals. The human experts are not likely to thoroughly examine these signals, and various machine sorting methods are used to aid the classification of the FAST candidates. In this study, we propose a new ensemble classification system for pulsar candidates. This system denotes the further development of the pulsar image-based classification system(PICS), which was used in the Arecibo Telescope pulsar survey, and has been retrained and customized for the FAST drift-scan survey. In this study, we designed a residual network model comprising 15 layers to replace the convolutional neural networks(CNNs) in PICS. The results of this study demonstrate that the new model can sort >96% of real pulsars to belong the top 1% of all candidates and classify >1.6 million candidates per day using a dual-GPU and 24-core computer. This increased speed and efficiency can help to facilitate real-time or quasi-real-time processing of the pulsar-search data stream obtained from CRAFTS. In addition, we have published the labeled FAST data used in this study online, which can aid in the development of new deep learning techniques for performing pulsar searches.

关键词： pulsars neural networks data analysis

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