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.