针对风力发电机叶片人工检测低效,缺陷诊断难的问题,提出一种基于无人机与图像处理的风力发电机叶片缺陷识别方法。通过Halcon 12与Visual Studio 2015的联合开发,实现图像处理流程、检测结果输出以及缺陷回放等功能,包括相机标定、通...针对风力发电机叶片人工检测低效,缺陷诊断难的问题,提出一种基于无人机与图像处理的风力发电机叶片缺陷识别方法。通过Halcon 12与Visual Studio 2015的联合开发,实现图像处理流程、检测结果输出以及缺陷回放等功能,包括相机标定、通过快速自适应加权中值滤波处理图像、动态阈值分割叶片图像缺陷特征,利用区域处理识别裂纹和砂眼等缺陷,并对缺陷进行分类与测量以及输出对叶片质量的分析报告等,实现风力发电机叶片表面缺陷的自动检测功能。通过实例验证了该方法在风力发电机叶片表面缺陷检测中的较高精确性与算法稳定性。展开更多
Astronomical imaging technologies are basic tools for the exploration of the universe,providing basic data for the research of astronomy and space physics.The Soft X-ray Imager(SXI)carried by the Solar wind Magnetosph...Astronomical imaging technologies are basic tools for the exploration of the universe,providing basic data for the research of astronomy and space physics.The Soft X-ray Imager(SXI)carried by the Solar wind Magnetosphere Ionosphere Link Explorer(SMILE)aims to capture two-dimensional(2-D)images of the Earth’s magnetosheath by using soft X-ray imaging.However,the observed 2-D images are affected by many noise factors,destroying the contained information,which is not conducive to the subsequent reconstruction of the three-dimensional(3-D)structure of the magnetopause.The analysis of SXI-simulated observation images shows that such damage cannot be evaluated with traditional restoration models.This makes it difficult to establish the mapping relationship between SXIsimulated observation images and target images by using mathematical models.We propose an image restoration algorithm for SXIsimulated observation images that can recover large-scale structure information on the magnetosphere.The idea is to train a patch estimator by selecting noise–clean patch pairs with the same distribution through the Classification–Expectation Maximization algorithm to achieve the restoration estimation of the SXI-simulated observation image,whose mapping relationship with the target image is established by the patch estimator.The Classification–Expectation Maximization algorithm is used to select multiple patch clusters with the same distribution and then train different patch estimators so as to improve the accuracy of the estimator.Experimental results showed that our image restoration algorithm is superior to other classical image restoration algorithms in the SXI-simulated observation image restoration task,according to the peak signal-to-noise ratio and structural similarity.The restoration results of SXI-simulated observation images are used in the tangent fitting approach and the computed tomography approach toward magnetospheric reconstruction techniques,significantly improving the reconstruction results.Hence,the pro展开更多
In the background of“double carbon,”vigorously developing new energy is particularly important.Wind power is an important clean energy source.In the field of new energy,wind power scale is also expanding.With the wi...In the background of“double carbon,”vigorously developing new energy is particularly important.Wind power is an important clean energy source.In the field of new energy,wind power scale is also expanding.With the wind turbine,the probability of large-scale blade damage is also increasing.Because the large wind turbine blade crack detection cost is high and because of the poor working environment,this paper proposes a wind turbine blade surface defect detection method based on UAV acquisition images and digital image pro-cessing.The application of weighted averages to achieve grayscale processing,followed by median filtering to achieve image noise reduction,and an improved histogram equalization algorithm is proposed and used for the characteristics of the UAV acquisition images,which enhances the image by limiting the contrast adaptive his-togram equalization algorithm to make the details at the target area and defects more clear and complete,and improves the detection efficiency.The detection of the blade surface is achieved by separating and extracting the feature information from the defects through image foreground segmentation,threshold processing,and framing by the connected domain.The validity and accuracy of the proposed method in leaf detection were verified by experiments.展开更多
文摘针对风力发电机叶片人工检测低效,缺陷诊断难的问题,提出一种基于无人机与图像处理的风力发电机叶片缺陷识别方法。通过Halcon 12与Visual Studio 2015的联合开发,实现图像处理流程、检测结果输出以及缺陷回放等功能,包括相机标定、通过快速自适应加权中值滤波处理图像、动态阈值分割叶片图像缺陷特征,利用区域处理识别裂纹和砂眼等缺陷,并对缺陷进行分类与测量以及输出对叶片质量的分析报告等,实现风力发电机叶片表面缺陷的自动检测功能。通过实例验证了该方法在风力发电机叶片表面缺陷检测中的较高精确性与算法稳定性。
基金supported by the National Natural Science Foundation of China(Grant Nos.42322408,42188101,41974211,and 42074202)the Key Research Program of Frontier Sciences,Chinese Academy of Sciences(Grant No.QYZDJ-SSW-JSC028)+1 种基金the Strategic Priority Program on Space Science,Chinese Academy of Sciences(Grant Nos.XDA15052500,XDA15350201,and XDA15014800)supported by the Youth Innovation Promotion Association of the Chinese Academy of Sciences(Grant No.Y202045)。
文摘Astronomical imaging technologies are basic tools for the exploration of the universe,providing basic data for the research of astronomy and space physics.The Soft X-ray Imager(SXI)carried by the Solar wind Magnetosphere Ionosphere Link Explorer(SMILE)aims to capture two-dimensional(2-D)images of the Earth’s magnetosheath by using soft X-ray imaging.However,the observed 2-D images are affected by many noise factors,destroying the contained information,which is not conducive to the subsequent reconstruction of the three-dimensional(3-D)structure of the magnetopause.The analysis of SXI-simulated observation images shows that such damage cannot be evaluated with traditional restoration models.This makes it difficult to establish the mapping relationship between SXIsimulated observation images and target images by using mathematical models.We propose an image restoration algorithm for SXIsimulated observation images that can recover large-scale structure information on the magnetosphere.The idea is to train a patch estimator by selecting noise–clean patch pairs with the same distribution through the Classification–Expectation Maximization algorithm to achieve the restoration estimation of the SXI-simulated observation image,whose mapping relationship with the target image is established by the patch estimator.The Classification–Expectation Maximization algorithm is used to select multiple patch clusters with the same distribution and then train different patch estimators so as to improve the accuracy of the estimator.Experimental results showed that our image restoration algorithm is superior to other classical image restoration algorithms in the SXI-simulated observation image restoration task,according to the peak signal-to-noise ratio and structural similarity.The restoration results of SXI-simulated observation images are used in the tangent fitting approach and the computed tomography approach toward magnetospheric reconstruction techniques,significantly improving the reconstruction results.Hence,the pro
文摘In the background of“double carbon,”vigorously developing new energy is particularly important.Wind power is an important clean energy source.In the field of new energy,wind power scale is also expanding.With the wind turbine,the probability of large-scale blade damage is also increasing.Because the large wind turbine blade crack detection cost is high and because of the poor working environment,this paper proposes a wind turbine blade surface defect detection method based on UAV acquisition images and digital image pro-cessing.The application of weighted averages to achieve grayscale processing,followed by median filtering to achieve image noise reduction,and an improved histogram equalization algorithm is proposed and used for the characteristics of the UAV acquisition images,which enhances the image by limiting the contrast adaptive his-togram equalization algorithm to make the details at the target area and defects more clear and complete,and improves the detection efficiency.The detection of the blade surface is achieved by separating and extracting the feature information from the defects through image foreground segmentation,threshold processing,and framing by the connected domain.The validity and accuracy of the proposed method in leaf detection were verified by experiments.
基金supported by the Inner Mongolia Autonomous Region Open Major Basic Research Project(Grant No.20120905)the National Natural Science Foundation of China(Grant No.51666014)
文摘Wind tunnel experiments of the wake characteristics of a two-blade wind turbine, in the downstream region of 0
