X-ray detection is the use of changes in the transmission intensity of X-rays before and after transmission to reflect the internal structural information of materials in the form of grayscale information. Once there are defects in the workpiece, the transmission intensity will change, resulting in a corresponding change in the grayscale value of the projected image.
X-ray detection is the use of changes in the transmission intensity of X-rays before and after transmission to reflect the internal structural information of materials in the form of grayscale information. Once there are defects in the workpiece, the transmission intensity will change, resulting in a corresponding change in the grayscale value of the projected image. Industrial X-ray machines combined with flat panel detectors, also known as digital radiography testing(DR), can directly display X-ray images on computer screens without the need for developing chemicals or intermediate scanning.Through filtering, enhancement and other operations, the defect area can be highlighted, background noise reduced, and the contrast and resolution of the image improved.In addition, computer vision and artificial intelligence technology can be used to automatically recognize and analyze images, achieving automatic defect detection and classification.
Following is the outline of automatic defect recognition(ADR):
1. Data collection and preparation: Obtain a series of detection images through X-ray inspection, and record the relevant information of each image, such as sample type, defect type, etc. These images will be used as datasets for algorithm training and testing.
2. Feature extraction: Using computer vision technology to extract meaningful features from X-ray detection images. These features include pixel values, grayscale values, texture, shape, edge information, etc. of the image.
3. Feature selection and dimension reduction: Select the most effective features from the extracted features and use dimension reduction algorithms to reduce the dimensions of the feature space, in order to improve the efficiency and accuracy of subsequent classification algorithms.
4. Training classification model: Input the extracted features and corresponding labels (with or without defects) into the machine learning algorithm for training.Common classification algorithms include Convolutional Neural Networks (CNN), Support Vector Machines (SVM), Decision Trees, etc.
5. Model optimization and evaluation: Optimize the parameters and structure of the model to improve the accuracy and robustness of classification. Evaluate the performance of the model through cross validation and evaluation metrics such as accuracy, recall, F1 value, etc.
6. Defect recognition and determination: Use a trained classification model to identify and determine defects in new X-ray detection images. By inputting the features of the image into the model, the model will automatically output judgment results to determine whether there are defects in the image.
It should be noted that automatic defect recognition of X-ray detection requires sufficient training samples and accurate labels, as well as appropriate feature extraction and classification algorithms. Meanwhile, the performance and accuracy of the model are also affected by data quality and preprocessing. Therefore, the specific implementation of the algorithm needs to be customized according to the specific application scenarios and data characteristics.