The use of spatial coding schemes is always a research hotspot of structural light 3D reconstruction. Spatial coding only needs one frame of image to reshape the three-dimensional feature of the object. However, it is difficult to obtain higher resolution due to fewer feature points extracted. In the coding stage, this paper uses a two-dimensional discrete pseudorandom pattern composed of rectangular color elements. And in the decoding stage, a feature detector for a rectangular grid point and a center point is proposed by using four corner points and a center point of a rectangle as feature points. It can get more feature points in the spatial coding without increasing the calculation amount during the decoding stage, thereby obtaining more accurate feature information on the surface of the object. From the experimental results, this method compared with the existing approaches can significantly improve the accuracy of rectangular grid points detection and can reconstruct more high-precision feature points.
It is beneficial for maintenance department to make maintenance strategy and reduce maintenance cost to forecast the hidden danger index value. In order to grasp the size information of High-speed railway wheel-set size in time and ensure the stable operation of high-speed railway, the size data of wheel-set are obtained by optical intercept image detection, and the LMBP neural network prediction model based on differential evolution is designed and implemented. The differential evolution algorithm (DE) is used to optimize the initial connection weights and thresholds between the layers of the neural network, and solve the problem that the back propagation (BP) network is easy to fall into the local extreme value due to the random initial connection weight and threshold. The Levenberg-Marquardt (LM) numerical algorithm is used to optimize the weights and thresholds in the BP network training process to solve the problem of long BP training time. According to the wheel diameter data of the CRH380 model, the effectiveness and accuracy of the method are verified by comparing the prediction results of different algorithms. Compared with the LMBP network and the standard BP network prediction model, the experimental results show that the DE-LMBP neural network model can obtain better correlation coefficients (0.9974), mean square error (0.0103), mean absolute error (0.0772) and average absolute percentage error (0.0084), which proves that the model is effective in predicting the size of the moving wheel and significantly improves the prediction accuracy.