基于CNN的十字像中心检测

十字线中心检测是反射法测量透镜中心偏的重要组成部分,十字中心的检测精度决定了透镜中心偏的测量精度。针对边缘不规则、对比度差、信噪比低的图像,提出了基于深度卷积神经网络的十字线中心检测算法。算法的思想是,卷积神经网络可以在一定程度上解决传统算法局限于提取十字像边缘直线和角点特征的问题,实现对十字像整体特征的识别与定位,这可以相对减小图像噪声对十字像中心定位的影响,从而实现在图像质量比较差的情况下对十字像中心准确定位。实验结果表明,提出的算法能够在图像边缘不规则、对比度差、信噪比低等的条件下比较精确得到十字线中心点。

Cross-center detection based on deep learning

Crossline center detection is an important part of reflective method for measuring lens center deviation. The detection accuracy of the cross center determines the measurement accuracy of the lens center-offset to some extent. Aiming at the image with irregular edge, poor contrast and low signal-to-noise ratio, a cross-line center detection algorithm based on depth convolution neural network is proposed. The idea of the algorithm is that the convolution neural network can solve the problem that the traditional algorithm is limited to extracting the line and corner features of the edge of the cross image to a certain extent, and realize the recognition and location of the overall features of the cross image. This can relatively reduce the impact of image noise on the location of the cross image center, so as to achieve the accurate location of the cross image center in the case of poor image quality. The experimental results show that the proposed algorithm can get the center of the cross line accurately under the conditions of irregular edges, poor contrast and low signal-to-noise ratio.