Research on Defect Detection Method for Composite Materials Based on Deep Learning Networks

Compared to traditional industrial materials, composites have higher durability and compressive strength. However, some components may have flaws due to the manufacturing process. Traditional defect detection methods have low accuracy and cannot adapt to complex shooting environments. Aiming to address the issues of high computational requirements in traditional detection models and the lack of lightweight detection capabilities, the Ghost module is used instead of convolutional arithmetic to construct a lightweight model. To reduce the computational complexity of the feature extraction module, we have incorporated an improved Efficient Channel Attention mechanism to improve the model's feature extraction capabilities. A rapid defect classification method is implemented to determine whether there are defects in the image or not by comparing the performance and running speed of models such as AlexNet, VGGNet, and ResNet. And ablation experiments are conducted for each model. The results show that the Ghost module model, which incorporates the improved Efficient Channel Attention mechanism, has a significant optimization effect on the convolutional neural network model. It can achieve a high accuracy rate when constructing lightweight models. It improves the running speed of the model, making it more efficient to use and deploy.