This paper mainly introduces some foreign research methods and fruits about weed identification by applying machine vision. This facet researches is lack in our country, this paper could be reference for domestic stud...This paper mainly introduces some foreign research methods and fruits about weed identification by applying machine vision. This facet researches is lack in our country, this paper could be reference for domestic studies about weed identification.展开更多
To fully leverage the advantages of mechanization and informatization in tunnel boring machine(TBM)operations,the authors aim to promote the advancement of tunnel construction technology toward intelligent development...To fully leverage the advantages of mechanization and informatization in tunnel boring machine(TBM)operations,the authors aim to promote the advancement of tunnel construction technology toward intelligent development.This involved exploring the deep integration of next-generation artificial intelligence technologies,such as sensing technology,automatic control technology,big data technology,deep learning,and machine vision,with key operational processes,including TBM excavation,direction adjustment,step changes,inverted arch block assembly,material transportation,and operation status assurance.The results of this integration are summarized as follows.(1)TBM key excavation parameter prediction algorithm was developed with an accuracy rate exceeding 90%.The TBM intelligent step-change control algorithm,based on machine vision,achieved an image segmentation accuracy rate of 95%and gripper shoe positioning error of±5 mm.(2)An automatic positioning system for inverted arch blocks was developed,enabling real-time perception of the spatial position and deviation during the assembly process.The system maintains an elevation positioning deviation within±3 mm and a horizontal positioning deviation within±10 mm,reducing the number of surveyors in each work team.(3)A TBM intelligent rail transportation system that achieves real-time human-machine positioning,automatic switch opening and closing,automatic obstacle avoidance,intelligent transportation planning,and integrated scheduling and command was designed.Each locomotive formation reduces one shunter and improves comprehensive transportation efficiency by more than 20%.(4)Intelligent analysis and prediction algorithms were developed to monitor and predict the trends of the hydraulic and gear oil parameters in real time,enhancing the proactive maintenance and system reliability.展开更多
For the autonomous guided vehicle (AGV) used mainly in unfixed work fields, a machine vision method was proposed for the navigation system, in which a series of navigation-signs are placed along the travel route. The ...For the autonomous guided vehicle (AGV) used mainly in unfixed work fields, a machine vision method was proposed for the navigation system, in which a series of navigation-signs are placed along the travel route. The navigation system detects and recognizes these signs, and accordingly informs the travel control system. In order for the navigation to have balanced ability of 1) covering a large area and 2) recognizing details of the sign, the proposed vision method was designed to be a hybrid one, using both the stereo vision and the traditional 2D template matching. The former implemented a coarse recognition function for above 1), and the later implemented a fine recognition function for above 2). The results from the coarse recognition were used in the fine recognition for the gaze control to input suitable 2D image of the signs. Experiments on a prototype system show the feasibility of the proposed hybrid method in achieving the objective specifications for a typical AGV.展开更多
An improved block matching approach to fast disparity estimation in machine vision applications is proposed, where the matching criterion is the sum of the absolute difference（SAD）.By evaluating the lower bounds, wh...An improved block matching approach to fast disparity estimation in machine vision applications is proposed, where the matching criterion is the sum of the absolute difference（SAD）.By evaluating the lower bounds, which become increasingly tighter for the matching criteria, the method tries to successively terminate unnecessary computations of the matching criteria between the reference block in one image and the ineligible candidate blocks in another image.It also eliminates the ineligible blocks as early as possible, while ensuring the optimal disparity of each pixel.Also, the proposed method can further speed up the elimination of ineligible candidate blocks by efficiently using the continuous constraint of disparity to predict the initial disparity of each pixel.The performance of the new algorithm is evaluated by carrying out a theoretical analysis, and by comparing its performance with the disparity estimation method based on the standard block matching.Simulated results demonstrate that the proposed algorithm achieves a computational cost reduction of over 50.5% in comparision with the standard block matching method.展开更多
The alignment coupling between optical waveguide chips and optical fiber arrays is the basis of the alignment coupling of planar optical waveguide devices, and the precise position detection with angle and spacing adj...The alignment coupling between optical waveguide chips and optical fiber arrays is the basis of the alignment coupling of planar optical waveguide devices, and the precise position detection with angle and spacing adjustments is one of the key steps of alignment coupling. A methodology for position detection, and angle and spacing adjustment was proposed for optical waveguide chips and optical fiber arrays based on machine vision. The experimental results show angle detection precision levels higher than 0.05°, line detection precision levels higher than 0.1 μm, and detection time less than 2 s. Therefore, the system developed herein meets the precise requirements necessary for position detection, and angle and spacing adjustments for optical waveguide chips and optical fiber arrays.展开更多
A detecting method based on machine vision was put forward to test the performance of seedmeter with corn and soybean seeds as test samples,in which MATLAB software was applied to process image data and analyze the re...A detecting method based on machine vision was put forward to test the performance of seedmeter with corn and soybean seeds as test samples,in which MATLAB software was applied to process image data and analyze the results.The experimental results showed that the mean value of absolute error of the sowing speed for soybean was 0.004-0.68 seed ？ s-1;the mean value of relative error was from 6.5% to 130%,and there were no significant differences of mean value,standard deviation and coefficient of variation of flowing seeds between manual statistics and MATLAB statistics.The machine vision method was proved to be time-saving,labor-saving and no-touching in the seedmeter precision detecting.展开更多
Low-rank matrix recovery is an important problem extensively studied in machine learning, data mining and computer vision communities. A novel method is proposed for low-rank matrix recovery, targeting at higher recov...Low-rank matrix recovery is an important problem extensively studied in machine learning, data mining and computer vision communities. A novel method is proposed for low-rank matrix recovery, targeting at higher recovery accuracy and stronger theoretical guarantee. Specifically, the proposed method is based on a nonconvex optimization model, by solving the low-rank matrix which can be recovered from the noisy observation. To solve the model, an effective algorithm is derived by minimizing over the variables alternately. It is proved theoretically that this algorithm has stronger theoretical guarantee than the existing work. In natural image denoising experiments, the proposed method achieves lower recovery error than the two compared methods. The proposed low-rank matrix recovery method is also applied to solve two real-world problems, i.e., removing noise from verification code and removing watermark from images, in which the images recovered by the proposed method are less noisy than those of the two compared methods.展开更多
文摘This paper mainly introduces some foreign research methods and fruits about weed identification by applying machine vision. This facet researches is lack in our country, this paper could be reference for domestic studies about weed identification.
文摘To fully leverage the advantages of mechanization and informatization in tunnel boring machine(TBM)operations,the authors aim to promote the advancement of tunnel construction technology toward intelligent development.This involved exploring the deep integration of next-generation artificial intelligence technologies,such as sensing technology,automatic control technology,big data technology,deep learning,and machine vision,with key operational processes,including TBM excavation,direction adjustment,step changes,inverted arch block assembly,material transportation,and operation status assurance.The results of this integration are summarized as follows.(1)TBM key excavation parameter prediction algorithm was developed with an accuracy rate exceeding 90%.The TBM intelligent step-change control algorithm,based on machine vision,achieved an image segmentation accuracy rate of 95%and gripper shoe positioning error of±5 mm.(2)An automatic positioning system for inverted arch blocks was developed,enabling real-time perception of the spatial position and deviation during the assembly process.The system maintains an elevation positioning deviation within±3 mm and a horizontal positioning deviation within±10 mm,reducing the number of surveyors in each work team.(3)A TBM intelligent rail transportation system that achieves real-time human-machine positioning,automatic switch opening and closing,automatic obstacle avoidance,intelligent transportation planning,and integrated scheduling and command was designed.Each locomotive formation reduces one shunter and improves comprehensive transportation efficiency by more than 20%.(4)Intelligent analysis and prediction algorithms were developed to monitor and predict the trends of the hydraulic and gear oil parameters in real time,enhancing the proactive maintenance and system reliability.
文摘For the autonomous guided vehicle (AGV) used mainly in unfixed work fields, a machine vision method was proposed for the navigation system, in which a series of navigation-signs are placed along the travel route. The navigation system detects and recognizes these signs, and accordingly informs the travel control system. In order for the navigation to have balanced ability of 1) covering a large area and 2) recognizing details of the sign, the proposed vision method was designed to be a hybrid one, using both the stereo vision and the traditional 2D template matching. The former implemented a coarse recognition function for above 1), and the later implemented a fine recognition function for above 2). The results from the coarse recognition were used in the fine recognition for the gaze control to input suitable 2D image of the signs. Experiments on a prototype system show the feasibility of the proposed hybrid method in achieving the objective specifications for a typical AGV.
基金supported by the Opening Project of State Key Laboratory for Manufacturing Systems EngineeringFoundation for Youth Teacher of School of Mechanical Engineering, Xi’an Jiaotong University Brain Korea 21(BK21) Program of Ministry of Education and Human Resources Development
文摘An improved block matching approach to fast disparity estimation in machine vision applications is proposed, where the matching criterion is the sum of the absolute difference（SAD）.By evaluating the lower bounds, which become increasingly tighter for the matching criteria, the method tries to successively terminate unnecessary computations of the matching criteria between the reference block in one image and the ineligible candidate blocks in another image.It also eliminates the ineligible blocks as early as possible, while ensuring the optimal disparity of each pixel.Also, the proposed method can further speed up the elimination of ineligible candidate blocks by efficiently using the continuous constraint of disparity to predict the initial disparity of each pixel.The performance of the new algorithm is evaluated by carrying out a theoretical analysis, and by comparing its performance with the disparity estimation method based on the standard block matching.Simulated results demonstrate that the proposed algorithm achieves a computational cost reduction of over 50.5% in comparision with the standard block matching method.
基金Projects(51475479,51075402)supported by the National Natural Science Foundation of ChinaProject(2012AA040406)supported by the National High Technology Research and Development Program of China+2 种基金Project(20110162130004)supported by the Research Fund for the Doctoral Program of Higher Education of ChinaProject(14JJ2010)supported by the Natural Science Foundation of Hunan Province,ChinaProject(GZKF-201401)supported by the Open Project of Stage Key Laboratory of Fluid Power Transmission and Control(Zhejiang University),China
文摘The alignment coupling between optical waveguide chips and optical fiber arrays is the basis of the alignment coupling of planar optical waveguide devices, and the precise position detection with angle and spacing adjustments is one of the key steps of alignment coupling. A methodology for position detection, and angle and spacing adjustment was proposed for optical waveguide chips and optical fiber arrays based on machine vision. The experimental results show angle detection precision levels higher than 0.05°, line detection precision levels higher than 0.1 μm, and detection time less than 2 s. Therefore, the system developed herein meets the precise requirements necessary for position detection, and angle and spacing adjustments for optical waveguide chips and optical fiber arrays.
基金Supported by Henan Institute of Science and Technology (055031)
文摘A detecting method based on machine vision was put forward to test the performance of seedmeter with corn and soybean seeds as test samples,in which MATLAB software was applied to process image data and analyze the results.The experimental results showed that the mean value of absolute error of the sowing speed for soybean was 0.004-0.68 seed ？ s-1;the mean value of relative error was from 6.5% to 130%,and there were no significant differences of mean value,standard deviation and coefficient of variation of flowing seeds between manual statistics and MATLAB statistics.The machine vision method was proved to be time-saving,labor-saving and no-touching in the seedmeter precision detecting.
基金Projects(61173122,61262032) supported by the National Natural Science Foundation of ChinaProjects(11JJ3067,12JJ2038) supported by the Natural Science Foundation of Hunan Province,China
文摘Low-rank matrix recovery is an important problem extensively studied in machine learning, data mining and computer vision communities. A novel method is proposed for low-rank matrix recovery, targeting at higher recovery accuracy and stronger theoretical guarantee. Specifically, the proposed method is based on a nonconvex optimization model, by solving the low-rank matrix which can be recovered from the noisy observation. To solve the model, an effective algorithm is derived by minimizing over the variables alternately. It is proved theoretically that this algorithm has stronger theoretical guarantee than the existing work. In natural image denoising experiments, the proposed method achieves lower recovery error than the two compared methods. The proposed low-rank matrix recovery method is also applied to solve two real-world problems, i.e., removing noise from verification code and removing watermark from images, in which the images recovered by the proposed method are less noisy than those of the two compared methods.
