基于光栅图像识别的目标定位优化方法研究

Research on optimization method of target location based on raster image recognition

高精度光栅测量系统和光谱识别定标对于大口径空间天文望远镜观测至关重要,将图像的智能识别与光栅测量系统相结合,可以解决传统光栅测试过程中目标识别困难和特征难以提取的问题。由于目标光源点光谱成像的特点以及背景噪声的干扰,图像目标的自动识别和位置提取精度受限。该文设计并构建基于观测图像识别的光栅测试分析系统,利用光栅的分光特性,结合图像传感器进行光电转换和特征识别,通过分析目标光栅图像的关键特征,利用图像预处理算法对光栅图像进行反色和模糊去噪,获得更清晰的光斑特征,通过密度质心法提取光斑中心,再由高斯曲面拟合提取光栅图像的目标中心和像素分布特征。实验结果表明,相比传统密度质心法,该文方法能够准确提取光栅图像中多目标的中心,像素幅值识别精度提升1个像素以上。

High-precision raster measurement system and spectral recognition calibration are essential for largeaperture space telescope observations.Combining intelligent image recognition with the raster measurement system can solve the problem of difficulty in target recognition and feature extraction in the traditional raster process.Due to the characteristics of spectral imaging of target light source points and the interference of background noise,the accuracy of automatic identification and location extraction of image targets is limited.This paper designs and constructs a raster test and analysis system based on observation image recognition.It uses the spectral characteristics of the raster and combines the image sensor to perform photoelectric conversion and feature recognition.By analyzing the key features of the target grating image,the image preprocessing algorithm is used to perform the raster image.Reverse color and blur and denoise to obtain a clearer spot feature.The spot center is extracted by the density centroid method,and then the target center and pixel distribution characteristics of the raster image are extracted by Gaussian surface fitting.Experimental results show that compared with the traditional density centroid method,this method can accurately extract the center of multiple targets in the raster image,and the pixel amplitude recognition accuracy is improved by more than 1 pixel.