本文選題：風力發(fā)電機葉片 + 攝影測量��； 參考：《湖南科技大學》2017年碩士論文

【摘要】：攝影測量技術(shù)由于其高精度、易操作、實時性及非接觸等優(yōu)點,逐步應(yīng)用于大型、復雜、柔性結(jié)構(gòu)的位移和振動測量。相機標定指在測量之前通過一定的方法建立物點像素坐標與其三維坐標之間的映射關(guān)系,所以標定直接決定標志點三維坐標的提取。風力發(fā)電機葉片是一種具有復雜曲面結(jié)構(gòu)的柔性體,其振動模態(tài)能反映整機運行狀態(tài)及潛在故障。本文結(jié)合風力發(fā)電機葉片振動測量的實際工程背景,針對雙目標定問題開展基于神經(jīng)網(wǎng)絡(luò)的標定方法研究,并且針對標定中角點的識別和定位精度問題開展了基于SV算子的亞像素級定位方法的研究,具體工作如下:第一:提出基于神經(jīng)網(wǎng)絡(luò)的虛擬靶標雙目標定方法。相機的標定精度很大程度上取決于標定板在相機視野中的覆蓋率,然而大面積的標定靶標制造加工困難而且不易操作�；诖�,本文提出了利用單角點靶標構(gòu)建虛擬立體靶標,并結(jié)合BP神經(jīng)網(wǎng)絡(luò)的非線性映射特性對雙目相機進行標定。實驗證明,該方法的標定精度的相對誤差為0.0445%,明顯較低于MATLAB標定工具箱0.1329%的標定誤差。第二:提出雙目標定過程中SV算子角點識別定位精度的改進方法。標定過程中,角點識別定位精度是相機標定精度的另一個重要影響因素。SV算子是針對棋盤格角點識別的一種主要方法,較其他特征點識別方法有其特殊性。本文在現(xiàn)有的SV算子角點識別定位的基礎(chǔ)上,增加了雙線性差值及質(zhì)心提取的方法,進而將定位精度提高到亞像素級。實驗證明,該角點識別定位的改進方法使相機標定的相對誤差由原來的0.0445%下降到0.0211%,整體平均誤差由原來的0.34727mm下降到0.16458mm,說明SV算子的改進方法有效地提高了標定精度。第三:針對本文所提出的標定方法及其改進方法的精度問題,搭建風力發(fā)電機葉片振動模態(tài)的雙目視覺攝影測量實驗臺,并分別采取本文方法與MATLAB標定工具箱對實驗結(jié)果進行分析,并通過葉片模態(tài)參數(shù)來評價本文標定方法的有效性。
[Abstract]:Photogrammetry has been gradually applied to the displacement and vibration measurement of large, complex and flexible structures because of its advantages of high precision, easy operation, real-time and non-contact. Camera calibration refers to the mapping relationship between pixel coordinates of object points and 3D coordinates before measurement, so calibration directly determines the extraction of 3D coordinates of marker points. The blade of wind turbine is a kind of flexible body with complex curved surface structure. Its vibration mode can reflect the running state and potential fault of the whole machine. Based on the practical engineering background of wind turbine blade vibration measurement, the calibration method based on neural network is studied in this paper. Aiming at the problem of corner recognition and location accuracy in calibration, the sub-pixel level localization method based on SV operator is studied. The main work is as follows: first, a method of virtual target double target location based on neural network is proposed. The calibration accuracy of the camera depends to a great extent on the coverage of the calibration board in the camera field of vision. However, the large area calibration target is difficult to manufacture and operate. Based on this, a virtual stereo target is constructed by using a single corner target, and the binocular camera is calibrated in combination with the nonlinear mapping characteristics of BP neural network. The experimental results show that the relative error of the calibration accuracy is 0.0445, which is obviously lower than the calibration error of 0.1329% in the MATLAB calibration toolbox. Second, an improved method for location accuracy of SV operator corner recognition in the process of double target determination is proposed. In the process of calibration, corner recognition positioning accuracy is another important factor affecting camera calibration accuracy. SV operator is a main method for chessboard grid corner recognition, which has its own particularity compared with other feature point recognition methods. Based on the existing SV operator corner recognition and location, the bilinear difference and centroid extraction methods are added in this paper, and the accuracy of location is improved to sub-pixel level. Experiments show that the improved method of corner recognition and positioning can reduce the relative error of camera calibration from 0.0445% to 0.02111.The overall average error is reduced from the original 0.34727mm to 0.16458mm, which shows that the improved method of SV operator can effectively improve the calibration accuracy. Thirdly, aiming at the accuracy of the calibration method and its improvement method, a binocular visual photogrammetry experiment bench is built for the vibration mode of wind turbine blade. The experimental results are analyzed by using the proposed method and the MATLAB calibration toolbox, and the effectiveness of the proposed calibration method is evaluated by the blade modal parameters.
【學位授予單位】：湖南科技大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TM315

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本文編號：1911574