A Defect Recognition and Classification Method Based on Improved Convolutional Neural Network and Terahertz Time-Domain Spectroscopy System

Convolutional neural networks CNN can perform defect recognition and classification, saving time compared to traditional methods. However, traditional CNN are difficult to achieve accurate differentiation due to insufficient feature extraction capability and low computational efficiency when dealing with scenes with complex backgrounds and similar defect categories. To solve these problems, this paper proposes an improved convolutional neural network based on multimodal data fusion to achieve efficient automated defect recognition and classification by combining the technical advantages of terahertz time-domain spectral system. Firstly, the spectral data of the samples are obtained by a terahertz time-domain spectroscopy system, and the pre-processed spectral data are imaged. Second, the absorption coefficients were obtained by building a terahertz propagation model inversion. Then, the terahertz absorption coefficients are deeply fused with the image data to construct a multimodal dataset as the network input. Convolutional blocks with multi-layer asymmetric convolutional kernels are designed in the convolutional layer to enhance the accuracy and classification speed of defect recognition by strengthening the feature extraction and learning capabilities. Meanwhile, jump connections are chosen between the convolutional blocks, aiming to resist the problems of gradient vanishing and overfitting. Numerical experiments show that the improved CNN achieves 99.4 accuracy in defect classification with an F1 score of 0.99 and 100 accuracy in the confusion matrix validation set. Compared with the traditional CNN, the accuracy is improved by 6 and the F1 score is improved by 4 , which provides a reliable technical support for defect recognition and classification in complex scenes.