# Research on Defect Detection Method for Composite Materials Based on Deep Learning Networks
###### Abstract  
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. defect detection; Ghost module; attention mechanism; rapid classification +
Footnote †: journal: Medicaland grayscale covariance matrix [6; 7; 8]. These methods can detect various irregularly shaped defects, although they are limited to detecting only one specific type of defect. Defect detection algorithms based on deep learning network models can overcome the shortcomings of traditional algorithms and adapt to various application scenarios [9; 10; 11; 12]. Commonly used methods include image classification, target detection tasks, and semantic segmentation tasks [13]. Convolutional neural networks (CNNs) have been improved and are now utilized in various defect detection applications [14; 15]. [PERSON] et al. constructed a multilayer convolutional neural network using the traditional CNN structure and utilized the concept of separate detection for the multi-classification of various defects in a dataset [16]. In the task of surface defect detection in industrial concrete, [PERSON] et al. utilized the Faster R-CNN target detection network for industry-specific detection and achieved improved detection results [17]. Deep learning-based methods effectively overcome challenges in detecting composite defects and demonstrate better adaptability to complex application scenarios, ensuring more accurate defect detection in composite materials [18; 19; 20]. This study focuses on various types of defects in composite materials, analyzing both semantic and morphological data. It analyzes the characteristics of semantic distribution in composite material images and principles for capturing different components. Combining traditional computer vision algorithms, the dataset for defect detection in composite materials is obtained through preprocessing. A fast classification model has been developed using a deep learning network to enhance efficiency and accuracy. The main research content of this paper includes the following:  
1. We designed a lightweight and highly accurate model for rapidly classifying defects in composite images. The Ghost module extracts features from the input image, calculating a reduced number of parameters. The module utilizes feature map redundancy by employing convolutional operations to generate a segment of the feature map. This is then followed by linear operations to duplicate and enlarge the feature map, thereby reducing the computational load. 2. We introduce and improve the Efficient Channel Attention (ECA) mechanism. By incorporating the ECA module into the feature extraction process, distinct weights are assigned to each channel of the feature map. The channel-based attention mechanism enhances the accuracy of the model in pattern recognition.