The Image Classification Method for Eddy Current Inspection of Titanium Alloy Plate Based on Parallel Sparse Filtering and Deep Forest

Abstract

Titanium plate has a vital position in many industrial fields due to its outstanding characteristics, and the eddy current detection technology can quickly and non-destructively detect the defects of titanium plate, which is one of the crucial methods of titanium plate defect non-destructive testing. However, in the actual detection process, eddy current detection imaging is inevitably affected by noise interference to varying degrees, concerning the accuracy of defect classification recognition. Therefore, this study has proposed a titanium plate eddy current detection image classification method based on parallel sparse filtering and deep forest, which realizes the detection image's sparse feature extraction and defect classification. Firstly, the parallel sparse filtering network is constructed by adding another direction's feature extraction operation to the traditional sparse filtering. The parallel sparse filtering network extracts more comprehensive sparse features from the detection image. Secondly, a deep forest network is built, and the Bayesian optimization algorithm is used to optimize the network's hyperparameters. Finally, the deep forest network with optimized hyperparameters is used to classify and recognize the titanium plate defect eddy current detection images. The experimental results show that the proposed method has better feature representation and feature relevance learning ability, has higher classification accuracy under different levels of noise interference, with a classification accuracy increase of 3.09–40.65% compared to other conventional methods, and has better robustness and anti-noise ability.