Weighted voting ensemble method for predicting workpiece imaging dimensional deviation based on monocular vision systems

Abstract

Due to the different heights and positions of all kinds of workpieces, the imaging dimension of workpieces in monocular vision systems exists in deviation. To reduce the deviation and improve the accuracy of dimensional measurement, a BRR-SVR-BPNN weighted voting ensemble learning deviation prediction algorithm is proposed. The proposed method combines the probability inference feature of Bayesian Ridge regression (BRR), the maximum interval penalty feature of support vector regression (SVR), and the nonlinear expression feature of the backpropagation neural network (BPNN). And an automatic image processing method based on Halcon is proposed to establish the datasets for the deviation prediction algorithm. The experimental results show that the proposed algorithm has well prediction effect, high robustness, and accuracy on deviation prediction. It can be easily applied to predict the imaging dimension deviation of various workpieces or other similar tasks to improve the accuracy of workpiece dimension measurement in the industrial field.