A Weld Defect Detection Method Based on Triplet Deep Neural Network
| Autor: | Danyu Lu, Jinhai Liu, Hongfei Zhu, Qu Fuming, Xiaoyuan Liu |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
0209 industrial biotechnology
Artificial neural network Computer science business.industry Feature vector Pattern recognition 02 engineering and technology Welding law.invention Support vector machine 020901 industrial engineering & automation law Nondestructive testing 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Artificial intelligence business |
| Zdroj: | 2020 Chinese Control And Decision Conference (CCDC). |
| DOI: | 10.1109/ccdc49329.2020.9164549 |
| Popis: | In industrial fields, Nondestructive Testing (NDT) has become an important method to test the quality of welds. For the low-contrast pipe weld defect x-ray image, the traditional detection method has low precision. In this paper, an automatic detection method for weld defects based on a triplet deep neural network is proposed. First, the original X-ray image is changed into a relief image, so that the feature of the defects is more obvious. Second, the feature vector is obtained by mapping the relief image through the triplet deep neural network. The deep neural network based on triplet makes the similar defect feature vectors are closer, and the distances of different defect feature vectors are farther. It is first time that the deep neural network based on triplet was used to detect the weld defect images. Finally, the weld defect was detected by Support Vector Machine (SVM) classifier. It is shown that the proposed detection method of weld defects has better performance than the conventional methods. |
| Databáze: | OpenAIRE |
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