Development of a CNN edge detection model of noised X-ray images for enhanced performance of non-destructive testing
| Autor: | Ki-Young Song, Madan M. Gupta, Zimu Xiao |
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| Rok vydání: | 2021 |
| Předmět: |
Similarity (geometry)
Computer science business.industry Applied Mathematics 020208 electrical & electronic engineering 010401 analytical chemistry ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Pattern recognition 02 engineering and technology Condensed Matter Physics 01 natural sciences Convolutional neural network Fuzzy logic Edge detection 0104 chemical sciences Image (mathematics) Nondestructive testing 0202 electrical engineering electronic engineering information engineering Development (differential geometry) Artificial intelligence Noise (video) Electrical and Electronic Engineering business Instrumentation |
| Zdroj: | Measurement. 174:109012 |
| ISSN: | 0263-2241 |
| DOI: | 10.1016/j.measurement.2021.109012 |
| Popis: | X-ray non-destructive testing (NDT) is a primary detection technology in industrial fields, providing an effective detection for fragile and complex structures without destructing components. In this study, we adopt the principle of convolutional neural network (CNN) and a Laplacian filter to propose an edge detection model with improved performance. By constructing X-ray image datasets with different noise levels, our proposed CNN model successfully detects fuzzy defects on noised X-ray images, and presents better structure similarity of the detected information compared to conventional edge detection algorithms, Canny and SUSAN. Additionally, the experiment results indicate that the noised training datasets effectively improves the model’s capability of noise resistance in edge detection tasks. Furthermore, the quality of training images significantly affects the performance of the trained model. This study develops a robust edge detection algorithm for low-cost and noise-independent X-ray non-destructive testing technology, providing a meaningful reference in edge detection of industrial X-ray images. |
| Databáze: | OpenAIRE |
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