Real-time and accurate drogue pose measurement during docking is basic and critical for Autonomous Aerial Refueling(AAR).Vision measurement is the best practicable technique,but its measurement accuracy and robustness...Real-time and accurate drogue pose measurement during docking is basic and critical for Autonomous Aerial Refueling(AAR).Vision measurement is the best practicable technique,but its measurement accuracy and robustness are easily affected by limited computing power of airborne equipment,complex aerial scenes and partial occlusion.To address the above challenges,we propose a novel drogue keypoint detection and pose measurement algorithm based on monocular vision,and realize real-time processing on airborne embedded devices.Firstly,a lightweight network is designed with structural re-parameterization to reduce computational cost and improve inference speed.And a sub-pixel level keypoints prediction head and loss functions are adopted to improve keypoint detection accuracy.Secondly,a closed-form solution of drogue pose is computed based on double spatial circles,followed by a nonlinear refinement based on Levenberg-Marquardt optimization.Both virtual simulation and physical simulation experiments have been used to test the proposed method.In the virtual simulation,the mean pixel error of the proposed method is 0.787 pixels,which is significantly superior to that of other methods.In the physical simulation,the mean relative measurement error is 0.788%,and the mean processing time is 13.65 ms on embedded devices.展开更多
In order to decrease vehicle crashes, a new rear view vehicle detection system based on monocular vision is designed. First, a small and flexible hardware platform based on a DM642 digtal signal processor （DSP） micr...In order to decrease vehicle crashes, a new rear view vehicle detection system based on monocular vision is designed. First, a small and flexible hardware platform based on a DM642 digtal signal processor （DSP） micro-controller is built. Then, a two-step vehicle detection algorithm is proposed. In the first step, a fast vehicle edge and symmetry fusion algorithm is used and a low threshold is set so that all the possible vehicles have a nearly 100% detection rate （TP） and the non-vehicles have a high false detection rate （FP）, i. e., all the possible vehicles can be obtained. In the second step, a classifier using a probabilistic neural network （PNN） which is based on multiple scales and an orientation Gabor feature is trained to classify the possible vehicles and eliminate the false detected vehicles from the candidate vehicles generated in the first step. Experimental results demonstrate that the proposed system maintains a high detection rate and a low false detection rate under different road, weather and lighting conditions.展开更多
Visual sensors are used to measure the relative state of the chaser spacecraft to the target spacecraft during close range ren- dezvous phases. This article proposes a two-stage iterative algorithm based on an inverse...Visual sensors are used to measure the relative state of the chaser spacecraft to the target spacecraft during close range ren- dezvous phases. This article proposes a two-stage iterative algorithm based on an inverse projection ray approach to address the relative position and attitude estimation by using feature points and monocular vision. It consists of two stages： absolute orienta- tion and depth recovery. In the first stage, Umeyama＇s algorithm is used to fit the three-dimensional （3D） model set and estimate the 3D point set while in the second stage, the depths of the observed feature points are estimated. This procedure is repeated until the result converges. Moreover, the effectiveness and convergence of the proposed algorithm are verified through theoreti- cal analysis and mathematical simulation.展开更多
In the laser displacement sensors measurement system,the laser beam direction is an important parameter.Particularly,the azimuth and pitch angles are the most important parameters to a laser beam.In this paper,based o...In the laser displacement sensors measurement system,the laser beam direction is an important parameter.Particularly,the azimuth and pitch angles are the most important parameters to a laser beam.In this paper,based on monocular vision,a laser beam direction measurement method is proposed.First,place the charge coupled device(CCD)camera above the base plane,and adjust and fix the camera position so that the optical axis is nearly perpendicular to the base plane.The monocular vision localization model is established by using circular aperture calibration board.Then the laser beam generating device is placed and maintained on the base plane at fixed position.At the same time a special target block is placed on the base plane so that the laser beam can project to the special target and form a laser spot.The CCD camera placed above the base plane can acquire the laser spot and the image of the target block clearly,so the two-dimensional(2D)image coordinate of the centroid of the laser spot can be extracted by correlation algorithm.The target is moved at an equal distance along the laser beam direction,and the spots and target images of each moving under the current position are collected by the CCD camera.By using the relevant transformation formula and combining the intrinsic parameters of the target block,the2D coordinates of the gravity center of the spot are converted to the three-dimensional(3D)coordinate in the base plane.Because of the moving of the target,the3D coordinates of the gravity center of the laser spot at different positions are obtained,and these3D coordinates are synthesized into a space straight line to represent the laser beam to be measured.In the experiment,the target parameters are measured by high-precision instruments,and the calibration parameters of the camera are calibrated by a high-precision calibration board to establish the corresponding positioning model.The measurement accuracy is mainly guaranteed by the monocular vision positioning accuracy and the gravity center extraction accuracy.The experimental results show the maximum error of the angle between laser beams reaches to0.04°and the maximum error of beam pitch angle reaches to0.02°.展开更多
A new visual measurement method is proposed to estimate three-dimensional （3D） position of the object on the floor based on a single camera. The camera fixed on a robot is in an inclined position with respect to the...A new visual measurement method is proposed to estimate three-dimensional （3D） position of the object on the floor based on a single camera. The camera fixed on a robot is in an inclined position with respect to the floor. A measurement model with the camera＇s extrinsic parameters such as the height and pitch angle is described. Single image of a chessboard pattern placed on the floor is enough to calibrate the camera＇s extrinsic parameters after the camera＇s intrinsic parameters are calibrated. Then the position of object on the floor can be computed with the measurement model. Furthermore, the height of object can be calculated with the paired-points in the vertical line sharing the same position on the floor. Compared to the conventional method used to estimate the positions on the plane, this method can obtain the 3D positions. The indoor experiment testifies the accuracy and validity of the proposed method.展开更多
A system for mobile robot localization and navigation was presented.With the proposed system,the robot can be located and navigated by a single landmark in a single image.And the navigation mode may be following-track...A system for mobile robot localization and navigation was presented.With the proposed system,the robot can be located and navigated by a single landmark in a single image.And the navigation mode may be following-track,teaching and playback,or programming.The basic idea is that the system computes the differences between the expected and the recognized position at each time and then controls the robot in a direction to reduce those differences.To minimize the robot sensor equipment,only one omnidirectional camera was used.Experiments in disturbing environments show that the presented algorithm is robust and easy to implement,without camera rectification.The rootmean-square error(RMSE) of localization is 1.4,cm,and the navigation error in teaching and playback is within 10,cm.展开更多
Vehicle anti-collision technique is a hot topic in the research area of Intelligent Transport System. The research on preceding vehicles detection and the distance measurement, which are the key techniques, makes grea...Vehicle anti-collision technique is a hot topic in the research area of Intelligent Transport System. The research on preceding vehicles detection and the distance measurement, which are the key techniques, makes great contributions to safe-driving. This paper presents a method which can be used to detect preceding vehicles and get the distance between own car and the car ahead. Firstly, an adaptive threshold method is used to get shadow feature, and a shadow!area merging approach is used to deal with the distortion of the shadow border. Region of interest(ROI) is obtained using shadow feature. Then in the ROI, symmetry feature is analyzed to verify whether there are vehicles and to locate the vehicles. Finally, using monocular vision distance measurement based on camera interior parameters and geometrical reasoning, we get the distance between own car and the preceding one. Experimental results show that the proposed method can detect the preceding vehicle effectively and get the distance between vehicles accurately.展开更多
A hierarchical mobile robot simultaneous localization and mapping (SLAM) method that allows us to obtain accurate maps was presented. The local map level is composed of a set of local metric feature maps that are guar...A hierarchical mobile robot simultaneous localization and mapping (SLAM) method that allows us to obtain accurate maps was presented. The local map level is composed of a set of local metric feature maps that are guaranteed to be statistically independent. The global level is a topological graph whose arcs are labeled with the relative location between local maps. An estimation of these relative locations is maintained with local map alignment algorithm, and more accurate estimation is calculated through a global minimization procedure using the loop closure constraint. The local map is built with Rao-Blackwellised particle filter (RBPF), where the particle filter is used to extending the path posterior by sampling new poses. The landmark position estimation and update is implemented through extended Kalman filter (EKF). Monocular vision mounted on the robot tracks the 3D natural point landmarks, which are structured with matching scale invariant feature transform (SIFT) feature pairs. The matching for multi-dimension SIFT features is implemented with a KD-tree in the time cost of O(lbN). Experiment results on Pioneer mobile robot in a real indoor environment show the superior performance of our proposed method.展开更多
A trajectory tracking method is presented for the visual navigation of the monocular mobile robot.The robot move along line trajectory drawn beforehand,recognized and stop on the stop-sign to finish special task.The r...A trajectory tracking method is presented for the visual navigation of the monocular mobile robot.The robot move along line trajectory drawn beforehand,recognized and stop on the stop-sign to finish special task.The robot uses a forward looking colorful digital camera to capture information in front of the robot,and by the use of HSI model partition the trajectory and the stop-sign out.Then the "sampling estimate" method was used to calculate the navigation parameters.The stop-sign is easily recognized and can identify 256 different signs.Tests indicate that the method can fit large-scale intensity of brightness and has more robustness and better real-time character.展开更多
Building fences to manage the cattle grazing can be very expensive;cost inefficient. These do not provide dynamic control over the area in which the cattle are grazing. Existing virtual fencing techniques for the cont...Building fences to manage the cattle grazing can be very expensive;cost inefficient. These do not provide dynamic control over the area in which the cattle are grazing. Existing virtual fencing techniques for the control of herds of cattle, based on polygon coordinate definition of boundaries is limited in the area of land mass coverage and dynamism. This work seeks to develop a more robust and an improved monocular vision based boundary avoidance for non-invasive stray control system for cattle, with a view to increase land mass coverage in virtual fencing techniques and dynamism. The monocular vision based depth estimation will be modeled using concept of global Fourier Transform (FT) and local Wavelet Transform (WT) of image structure of scenes (boundaries). The magnitude of the global Fourier Transform gives the dominant orientations and textual patterns of the image;while the local Wavelet Transform gives the dominant spectral features of the image and their spatial distribution. Each scene picture or image is defined by features v, which contain the set of global (FT) and local (WT) statistics of the image. Scenes or boundaries distances are given by estimating the depth D by means of the image features v. Sound cues of intensity equivalent to the magnitude of the depth D are applied to the animal ears as stimuli. This brings about the desired control as animals tend to move away from uncomfortable sounds.展开更多
Using machine vision to identify and sort scattered regular targets is an urgent problem to be solved in automated production lines.This study proposed a three-dimensional(3D)recognition method combining monocular vis...Using machine vision to identify and sort scattered regular targets is an urgent problem to be solved in automated production lines.This study proposed a three-dimensional(3D)recognition method combining monocular vision and machine learning algorithms.According to the color characteristics of the targets,to convert the original color picture into YCbCr mode and use the 2D Otsu algorithm to perform gray level image segmentation on the Cb channel.Then the Haar-feature training was carried out.The comparison of feature training and Haar method for Hough transform showed that the recognized time of Haar-feature AdaBoost trainer reached 31.00 ms,while its false recognized rate was 3.91%.The strong classifier was formed by weight combination,and the Hough contour transformation algorithm was set to correct the normal vector between plane coordinate and camera coordinate system.The monocular vision system ensured that the field of camera view had not obstructed while the dots were being struck.It was measured and calculated angles between targets and the horizontal plane which coordinate points of the identified plane feature.The testing results were compared with the Otsu and AdaBoost trainer where the prediction and training set have an error of no more than 0.25 mm.Its correct rate can reach 95%.It shows that the Otsu and Haar-feature based on AdaBoost algorithm is feasible within a certain error ranges and meet the engineering requirements for solving the poses of automated regular three-dimensional targets.展开更多
The monocular vision-based system can obtain the leaf wall area characterizing the canopy parameter for online detection and real-time variable spraying,aiming to improve the accuracy of orchard spraying equipment and...The monocular vision-based system can obtain the leaf wall area characterizing the canopy parameter for online detection and real-time variable spraying,aiming to improve the accuracy of orchard spraying equipment and the utilization efficiency of pesticide.This study established a spraying system,in which canopy parameters were collected by monocular vision,and the spray volume decision coefficient was constructed by the leaf wall area and the L^(*)value in International Commission on Illumination Lab color space to control the duty cycle of each solenoid valve to achieve variable spraying.Four spray flow models were designed to determine the spray volume decision coefficient.The coefficients of determination of the spray volumes with the duty cycle range of 15%to 65%were all over 94 and the error of the leaf wall area values obtained using the improved super green algorithm(calculated as ExG=2.1G–1.1R–1.1B)was only 0.5%.The test showed that there is a negative relationship between canopy denseness and L^(*),and the value of L^(*)is smaller in the dense area compared with the sparse area;the actual flow generated by the system is similar to the theoretical flow when the duty cycle is 65%.The field validation tests showed that the variable spraying system could refine the droplet size and increase the droplet density to a certain extent with the same coverage rate,which had advantages over the continuous spraying.In terms of droplet deposition,DV0.1 and DV0.9 were reduced by 2μm and 18μm,respectively,and the increase of droplet density to 75 droplets/cm2.At the same time,the improvement of droplet distribution uniformity and droplet penetration by 16%and 3%,respectively.Compared with continuous spraying,variable spraying can achieve 55.64%savings.The study demonstrates the feasibility of monocular vision in guiding spraying operations and provides a reference for the use of monocular vision in plant protection operations.展开更多
Drogue recognition and 3D locating is a key problem during the docking phase of the autonomous aerial refueling （AAR）. To solve this problem, a novel and effective method based on monocular vision is presented in th...Drogue recognition and 3D locating is a key problem during the docking phase of the autonomous aerial refueling （AAR）. To solve this problem, a novel and effective method based on monocular vision is presented in this paper. Firstly, by employing computer vision with red-ring-shape feature, a drogue detection and recognition algorithm is proposed to guarantee safety and ensure the robustness to the drogue diversity and the changes in environmental condi- tions, without using a set of infrared light emitting diodes （LEDs） on the parachute part of the dro- gue. Secondly, considering camera lens distortion, a monocular vision measurement algorithm for drogue 3D locating is designed to ensure the accuracy and real-time performance of the system, with the drogue attitude provided. Finally, experiments are conducted to demonstrate the effective- ness of the proposed method. Experimental results show the performances of the entire system in contrast with other methods, which validates that the proposed method can recognize and locate the drogue three dimensionally, rapidly and precisely.展开更多
针对菠萝采摘作业中劳动力需求大以及采摘效率低等问题,对单目视觉目标识别和定位菠萝目标的抱扭式菠萝采摘机进行了研究。该菠萝采摘机由底盘、采摘机构、运动机构、机器视觉机构和运输机构组成;机器视觉系统选用Raspberry pi 3B作为...针对菠萝采摘作业中劳动力需求大以及采摘效率低等问题,对单目视觉目标识别和定位菠萝目标的抱扭式菠萝采摘机进行了研究。该菠萝采摘机由底盘、采摘机构、运动机构、机器视觉机构和运输机构组成;机器视觉系统选用Raspberry pi 3B作为图像处理器,并装载YOLO算法作为菠萝识别的分类器。采摘机构模拟人工采摘时抱紧扭转的动作行为,通过气缸推动V型机械爪进退实现对菠萝的夹持,通过控制舵机转动完成菠萝的扭断,采摘后将果实通过运输机构运送到包装点。研制了菠萝采摘机样机,利用样机进行了实验室模拟采摘实验。实验结果证明,该菠萝采摘机的视觉系统能够快速判断菠萝的位置,采摘机构夹持定位准确,可顺利完成采摘动作。该样机的研制为菠萝采摘提供了可行的技术参考。展开更多
基金supported by the National Science Fund for Distinguished Young Scholars,China(No.51625501)Aeronautical Science Foundation of China(No.20240046051002)National Natural Science Foundation of China(No.52005028).
文摘Real-time and accurate drogue pose measurement during docking is basic and critical for Autonomous Aerial Refueling(AAR).Vision measurement is the best practicable technique,but its measurement accuracy and robustness are easily affected by limited computing power of airborne equipment,complex aerial scenes and partial occlusion.To address the above challenges,we propose a novel drogue keypoint detection and pose measurement algorithm based on monocular vision,and realize real-time processing on airborne embedded devices.Firstly,a lightweight network is designed with structural re-parameterization to reduce computational cost and improve inference speed.And a sub-pixel level keypoints prediction head and loss functions are adopted to improve keypoint detection accuracy.Secondly,a closed-form solution of drogue pose is computed based on double spatial circles,followed by a nonlinear refinement based on Levenberg-Marquardt optimization.Both virtual simulation and physical simulation experiments have been used to test the proposed method.In the virtual simulation,the mean pixel error of the proposed method is 0.787 pixels,which is significantly superior to that of other methods.In the physical simulation,the mean relative measurement error is 0.788%,and the mean processing time is 13.65 ms on embedded devices.
基金The National Key Technology R&D Program of China during the 11th Five-Year Plan Period(2009BAG13A04)Jiangsu Transportation Science Research Program(No.08X09)Program of Suzhou Science and Technology(No.SG201076)
文摘In order to decrease vehicle crashes, a new rear view vehicle detection system based on monocular vision is designed. First, a small and flexible hardware platform based on a DM642 digtal signal processor （DSP） micro-controller is built. Then, a two-step vehicle detection algorithm is proposed. In the first step, a fast vehicle edge and symmetry fusion algorithm is used and a low threshold is set so that all the possible vehicles have a nearly 100% detection rate （TP） and the non-vehicles have a high false detection rate （FP）, i. e., all the possible vehicles can be obtained. In the second step, a classifier using a probabilistic neural network （PNN） which is based on multiple scales and an orientation Gabor feature is trained to classify the possible vehicles and eliminate the false detected vehicles from the candidate vehicles generated in the first step. Experimental results demonstrate that the proposed system maintains a high detection rate and a low false detection rate under different road, weather and lighting conditions.
基金Program for Changjiang Scholars and Innovative Research Team in University (IRT0520)Ph.D.Programs Foundation of Ministry of Education of China (20070213055)
文摘Visual sensors are used to measure the relative state of the chaser spacecraft to the target spacecraft during close range ren- dezvous phases. This article proposes a two-stage iterative algorithm based on an inverse projection ray approach to address the relative position and attitude estimation by using feature points and monocular vision. It consists of two stages： absolute orienta- tion and depth recovery. In the first stage, Umeyama＇s algorithm is used to fit the three-dimensional （3D） model set and estimate the 3D point set while in the second stage, the depths of the observed feature points are estimated. This procedure is repeated until the result converges. Moreover, the effectiveness and convergence of the proposed algorithm are verified through theoreti- cal analysis and mathematical simulation.
基金National Science and Technology Major Project of China(No.2016ZX04003001)Tianjin Research Program of Application Foundation and Advanced Technology(No.14JCZDJC39700)
文摘In the laser displacement sensors measurement system,the laser beam direction is an important parameter.Particularly,the azimuth and pitch angles are the most important parameters to a laser beam.In this paper,based on monocular vision,a laser beam direction measurement method is proposed.First,place the charge coupled device(CCD)camera above the base plane,and adjust and fix the camera position so that the optical axis is nearly perpendicular to the base plane.The monocular vision localization model is established by using circular aperture calibration board.Then the laser beam generating device is placed and maintained on the base plane at fixed position.At the same time a special target block is placed on the base plane so that the laser beam can project to the special target and form a laser spot.The CCD camera placed above the base plane can acquire the laser spot and the image of the target block clearly,so the two-dimensional(2D)image coordinate of the centroid of the laser spot can be extracted by correlation algorithm.The target is moved at an equal distance along the laser beam direction,and the spots and target images of each moving under the current position are collected by the CCD camera.By using the relevant transformation formula and combining the intrinsic parameters of the target block,the2D coordinates of the gravity center of the spot are converted to the three-dimensional(3D)coordinate in the base plane.Because of the moving of the target,the3D coordinates of the gravity center of the laser spot at different positions are obtained,and these3D coordinates are synthesized into a space straight line to represent the laser beam to be measured.In the experiment,the target parameters are measured by high-precision instruments,and the calibration parameters of the camera are calibrated by a high-precision calibration board to establish the corresponding positioning model.The measurement accuracy is mainly guaranteed by the monocular vision positioning accuracy and the gravity center extraction accuracy.The experimental results show the maximum error of the angle between laser beams reaches to0.04°and the maximum error of beam pitch angle reaches to0.02°.
基金supported by National Natural Science Foundation of China(Nos.61273352 and 61473295)National High Technology Research and Development Program of China(863 Program)(No.2015AA042307)Beijing Natural Science Foundation(No.4161002)
文摘A new visual measurement method is proposed to estimate three-dimensional （3D） position of the object on the floor based on a single camera. The camera fixed on a robot is in an inclined position with respect to the floor. A measurement model with the camera＇s extrinsic parameters such as the height and pitch angle is described. Single image of a chessboard pattern placed on the floor is enough to calibrate the camera＇s extrinsic parameters after the camera＇s intrinsic parameters are calibrated. Then the position of object on the floor can be computed with the measurement model. Furthermore, the height of object can be calculated with the paired-points in the vertical line sharing the same position on the floor. Compared to the conventional method used to estimate the positions on the plane, this method can obtain the 3D positions. The indoor experiment testifies the accuracy and validity of the proposed method.
基金Supported by National Natural Science Foundation of China (No. 31000422 and No. 61201081)Tianjin Municipal Education Commission(No.20110829)Tianjin Science and Technology Committee(No. 10JCZDJC22800)
文摘A system for mobile robot localization and navigation was presented.With the proposed system,the robot can be located and navigated by a single landmark in a single image.And the navigation mode may be following-track,teaching and playback,or programming.The basic idea is that the system computes the differences between the expected and the recognized position at each time and then controls the robot in a direction to reduce those differences.To minimize the robot sensor equipment,only one omnidirectional camera was used.Experiments in disturbing environments show that the presented algorithm is robust and easy to implement,without camera rectification.The rootmean-square error(RMSE) of localization is 1.4,cm,and the navigation error in teaching and playback is within 10,cm.
基金Key Projects in the Tianjin Science & Technology Pillay Program
文摘Vehicle anti-collision technique is a hot topic in the research area of Intelligent Transport System. The research on preceding vehicles detection and the distance measurement, which are the key techniques, makes great contributions to safe-driving. This paper presents a method which can be used to detect preceding vehicles and get the distance between own car and the car ahead. Firstly, an adaptive threshold method is used to get shadow feature, and a shadow!area merging approach is used to deal with the distortion of the shadow border. Region of interest(ROI) is obtained using shadow feature. Then in the ROI, symmetry feature is analyzed to verify whether there are vehicles and to locate the vehicles. Finally, using monocular vision distance measurement based on camera interior parameters and geometrical reasoning, we get the distance between own car and the preceding one. Experimental results show that the proposed method can detect the preceding vehicle effectively and get the distance between vehicles accurately.
基金The National High Technology Research and Development Program (863) of China (No2006AA04Z259)The National Natural Sci-ence Foundation of China (No60643005)
文摘A hierarchical mobile robot simultaneous localization and mapping (SLAM) method that allows us to obtain accurate maps was presented. The local map level is composed of a set of local metric feature maps that are guaranteed to be statistically independent. The global level is a topological graph whose arcs are labeled with the relative location between local maps. An estimation of these relative locations is maintained with local map alignment algorithm, and more accurate estimation is calculated through a global minimization procedure using the loop closure constraint. The local map is built with Rao-Blackwellised particle filter (RBPF), where the particle filter is used to extending the path posterior by sampling new poses. The landmark position estimation and update is implemented through extended Kalman filter (EKF). Monocular vision mounted on the robot tracks the 3D natural point landmarks, which are structured with matching scale invariant feature transform (SIFT) feature pairs. The matching for multi-dimension SIFT features is implemented with a KD-tree in the time cost of O(lbN). Experiment results on Pioneer mobile robot in a real indoor environment show the superior performance of our proposed method.
基金supported by a grant from the National High Technology Research and Development Program of China (863 Program)(No.2002AA420110-3)the key project of the State Grid Corporation of China (SGKJ[2007]159)
文摘A trajectory tracking method is presented for the visual navigation of the monocular mobile robot.The robot move along line trajectory drawn beforehand,recognized and stop on the stop-sign to finish special task.The robot uses a forward looking colorful digital camera to capture information in front of the robot,and by the use of HSI model partition the trajectory and the stop-sign out.Then the "sampling estimate" method was used to calculate the navigation parameters.The stop-sign is easily recognized and can identify 256 different signs.Tests indicate that the method can fit large-scale intensity of brightness and has more robustness and better real-time character.
文摘Building fences to manage the cattle grazing can be very expensive;cost inefficient. These do not provide dynamic control over the area in which the cattle are grazing. Existing virtual fencing techniques for the control of herds of cattle, based on polygon coordinate definition of boundaries is limited in the area of land mass coverage and dynamism. This work seeks to develop a more robust and an improved monocular vision based boundary avoidance for non-invasive stray control system for cattle, with a view to increase land mass coverage in virtual fencing techniques and dynamism. The monocular vision based depth estimation will be modeled using concept of global Fourier Transform (FT) and local Wavelet Transform (WT) of image structure of scenes (boundaries). The magnitude of the global Fourier Transform gives the dominant orientations and textual patterns of the image;while the local Wavelet Transform gives the dominant spectral features of the image and their spatial distribution. Each scene picture or image is defined by features v, which contain the set of global (FT) and local (WT) statistics of the image. Scenes or boundaries distances are given by estimating the depth D by means of the image features v. Sound cues of intensity equivalent to the magnitude of the depth D are applied to the animal ears as stimuli. This brings about the desired control as animals tend to move away from uncomfortable sounds.
基金This work was financially supported by the National Natural Science Foundation of China(Grant No.51705365)The authors also acknowledge the State Key Research Program of China(Grant No.2017YFD0700404)+1 种基金the Guangdong Provincial Department of Education Project(Grant No.2016KZDXM027)the Guangdong Provincial Department of Agriculture(Grant No.2019KJ129).
文摘Using machine vision to identify and sort scattered regular targets is an urgent problem to be solved in automated production lines.This study proposed a three-dimensional(3D)recognition method combining monocular vision and machine learning algorithms.According to the color characteristics of the targets,to convert the original color picture into YCbCr mode and use the 2D Otsu algorithm to perform gray level image segmentation on the Cb channel.Then the Haar-feature training was carried out.The comparison of feature training and Haar method for Hough transform showed that the recognized time of Haar-feature AdaBoost trainer reached 31.00 ms,while its false recognized rate was 3.91%.The strong classifier was formed by weight combination,and the Hough contour transformation algorithm was set to correct the normal vector between plane coordinate and camera coordinate system.The monocular vision system ensured that the field of camera view had not obstructed while the dots were being struck.It was measured and calculated angles between targets and the horizontal plane which coordinate points of the identified plane feature.The testing results were compared with the Otsu and AdaBoost trainer where the prediction and training set have an error of no more than 0.25 mm.Its correct rate can reach 95%.It shows that the Otsu and Haar-feature based on AdaBoost algorithm is feasible within a certain error ranges and meet the engineering requirements for solving the poses of automated regular three-dimensional targets.
基金supported by Guangzhou Science and Technology Plan Project(Grant No.202002030245)supported by the National Natural Science Foundation of China(Grant No.31671591,31971797)+4 种基金Guangdong Province Modern Agricultural Key Technology Model Integration and Demonstration and Promotion Project(2021)Guangdong Province Modern Agricultural Industry Technology System Innovation Team Construction Special Fund(Grant No.2021KJ108)Guangdong Provincial Education Department Special Innovation Category Project(Grant No.2019KTSCX013)Guangdong Provincial Science and Technology Innovation Strategy Special Funds in 2020(“Climbing Plan”Special Funds,Grant No.pdjh2020a0084)Guangdong Provincial Students’Innovation and Entrepreneurship Project in 2020(Grant No.S202010564150)。
文摘The monocular vision-based system can obtain the leaf wall area characterizing the canopy parameter for online detection and real-time variable spraying,aiming to improve the accuracy of orchard spraying equipment and the utilization efficiency of pesticide.This study established a spraying system,in which canopy parameters were collected by monocular vision,and the spray volume decision coefficient was constructed by the leaf wall area and the L^(*)value in International Commission on Illumination Lab color space to control the duty cycle of each solenoid valve to achieve variable spraying.Four spray flow models were designed to determine the spray volume decision coefficient.The coefficients of determination of the spray volumes with the duty cycle range of 15%to 65%were all over 94 and the error of the leaf wall area values obtained using the improved super green algorithm(calculated as ExG=2.1G–1.1R–1.1B)was only 0.5%.The test showed that there is a negative relationship between canopy denseness and L^(*),and the value of L^(*)is smaller in the dense area compared with the sparse area;the actual flow generated by the system is similar to the theoretical flow when the duty cycle is 65%.The field validation tests showed that the variable spraying system could refine the droplet size and increase the droplet density to a certain extent with the same coverage rate,which had advantages over the continuous spraying.In terms of droplet deposition,DV0.1 and DV0.9 were reduced by 2μm and 18μm,respectively,and the increase of droplet density to 75 droplets/cm2.At the same time,the improvement of droplet distribution uniformity and droplet penetration by 16%and 3%,respectively.Compared with continuous spraying,variable spraying can achieve 55.64%savings.The study demonstrates the feasibility of monocular vision in guiding spraying operations and provides a reference for the use of monocular vision in plant protection operations.
基金supported by the National Natural Science Foundation of China(Nos.61473307,61304120)
文摘Drogue recognition and 3D locating is a key problem during the docking phase of the autonomous aerial refueling （AAR）. To solve this problem, a novel and effective method based on monocular vision is presented in this paper. Firstly, by employing computer vision with red-ring-shape feature, a drogue detection and recognition algorithm is proposed to guarantee safety and ensure the robustness to the drogue diversity and the changes in environmental condi- tions, without using a set of infrared light emitting diodes （LEDs） on the parachute part of the dro- gue. Secondly, considering camera lens distortion, a monocular vision measurement algorithm for drogue 3D locating is designed to ensure the accuracy and real-time performance of the system, with the drogue attitude provided. Finally, experiments are conducted to demonstrate the effective- ness of the proposed method. Experimental results show the performances of the entire system in contrast with other methods, which validates that the proposed method can recognize and locate the drogue three dimensionally, rapidly and precisely.
文摘针对菠萝采摘作业中劳动力需求大以及采摘效率低等问题,对单目视觉目标识别和定位菠萝目标的抱扭式菠萝采摘机进行了研究。该菠萝采摘机由底盘、采摘机构、运动机构、机器视觉机构和运输机构组成;机器视觉系统选用Raspberry pi 3B作为图像处理器,并装载YOLO算法作为菠萝识别的分类器。采摘机构模拟人工采摘时抱紧扭转的动作行为,通过气缸推动V型机械爪进退实现对菠萝的夹持,通过控制舵机转动完成菠萝的扭断,采摘后将果实通过运输机构运送到包装点。研制了菠萝采摘机样机,利用样机进行了实验室模拟采摘实验。实验结果证明,该菠萝采摘机的视觉系统能够快速判断菠萝的位置,采摘机构夹持定位准确,可顺利完成采摘动作。该样机的研制为菠萝采摘提供了可行的技术参考。
