Prof. Wenbo Dong Profile

Prof. Wenbo Dong

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Publications (3)

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Proceedings Article | 31 May 2023 Paper

Simultaneous measurement method of six-degree-of-freedom absolute position and attitude based on light spots

Rui Ma, Jianquan Zhang, Wenbo Dong, Jiaqi Yu, Jiancheng Liu, Wenqi Ge, Jianguo He, Mengxi Yu, Aimin Xiao

Proceedings Volume 12711, 127110H (2023) https://doi.org/10.1117/12.2684020

KEYWORDS: Monte Carlo methods, Reflection, Motion measurement, Interferometers, Stochastic processes, Computer simulations, Retroreflectors, Incident light, Beam path, Photodetectors

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This article presents a novel method to simultaneously measure the six-degree-of-freedom (6-DOF) absolute position and attitude based on light spots. The proposed system consists of a measurement unit and a moving target: the measurement unit contains a laser, three cube corner retroreflectors (CCR), three CMOSs, and some beam splitters; the target is a cube with three CCRs installed on each of its three orthogonal planes. In the measurement unit, the laser is split into three reference lights as well as three measured lights which are detected by three CMOSs after returning from six CCRs. Based on the vector analysis of the optical path, the relationship between 6-DOF position and attitude of the moving target and the output coordinates of three CMOSs is established. This method is capable of simultaneously measuring translational motions along as well as rotational motions around three orthogonal axes and achieving the absolute positioning of the target, which has overcome the shortage that the measurement systems based on laser interference can not measure absolute position and attitude. The accuracy of this method has been verified by Monte Carlo stochastic simulation and sinusoidal trajectory simulation in the range of the target’s motion. The simulation results show that the errors of position are less than 0.5 μm and the errors of attitude are less than 2.3 ″, which indicates the algorithm error is no more than the minimum pixel size of CMOS. This 6-DOF absolute pose simultaneous measurement method with simplicity and high precision has great potential for application in various precision machining fields.

Proceedings Article | 28 November 2018 Paper

High-level semantic information extraction of remote-sensing images based on deep-learning image classification

Mengzhe Cheng, Junli Yang, Zhenyu Wang, Mengxi Yu, Wenbo Dong

Proceedings Volume 10817, 1081719 (2018) https://doi.org/10.1117/12.2502545

KEYWORDS: Remote sensing, Image classification, Target recognition, Machine vision, Machine learning

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In recent years, remote sensing imaging technology has developed rapidly. A growing number of high resolution remote sensing images become available, which largely facilitates the research and applications of remote sensing images. Landcover classification is one of the most important tasks of remote sensing image applications ^[1]. However, traditional classification methods rely on manual feature design, which is time-consuming and requires expertise. It is difficult to apply to massive data. Compared with the traditional classification methods, deep learning ^[2] can automatically acquire the most intrinsic and discriminative features of the image. Based on the deep learning image classification, this paper designs a high-level semantic information extraction system with high efficiency and robustness. A deep fully convolutional networks (FCN) is designed to extract the features from remote sensing images and to predict the landcover classes of each image, which include building, tree, road, and grass. On the basis of the classification results, we use binarization to highlight the building objects. Then the noise of the binarized image is removed by Gaussian filtering and morphological image processing. After that we set a threshold to delete small misdiagnosis areas. At last the connected domain algorithm is applied to detect the buildings and calculate the building number in each image. The forest coverage is then obtained by computing the proportion of the pixels with ‘tree’ class label to the total number of the pixels in each image. Different from the traditional image interpretation method, this systematic high-level semantic information extraction framework not only detects the number of buildings in the scene but also extracts forest coverage. Moreover, more high-level information extraction can be easily supplemented to this framework, such as road localization or interested object detection.

Proceedings Article | 15 November 2017 Paper

Navigation system based on machine vision of multiple reference markers

Xiaopeng Su, Wenbo Dong, Zhenyu Wang, Yuanyuan Zhou

Proceedings Volume 10605, 106053V (2017) https://doi.org/10.1117/12.2295880

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The position and attitude measurement of space object is a key problem in the field of real-time navigation, modern control and motion tracking. As a non-contact position and attitude estimation method, machine vision position and attitude estimation has the advantages of simple structure and convenient measurement. This paper presents a vision positioning system and method based on multiple reference markers. The camera moving along the object continuously collects images containing reference markers from the camera's field of view．The spatial position information of reference mark is determined in advance, and the position and direction of moving target are calculated according to location and attitude algorithm. The main contribution of this paper: first, a plurality of reference markers is arranged in the range of moving objects so as to enlarge the range of visual positioning; second, when more than one reference marker appears in the field of view, it is possible to improve the positioning accuracy by selecting the marker of the larger contour area or the marker of the distance closer to the imaging plane principal point; third, we use the decoder to transform the reference marker into digital number. This method can improve the robustness of the system.

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