Abstract
Deep learning techniques are used to identify weld image defects in the process of image defect recognition. In this paper, a transfer learning method based on convolutional neural networks is proposed for the recognition problem of deep neural network models on weld flaw detection image data sets. Designing interdomain heterogeneous transfer learning with the pretrained model on the large data set, the interdomain heterogeneous transfer learning is used to transfer the pretrained model in the source data domain to the weld inspection image data set according to the difference of the content in the source and target data domains, and the effectiveness of the transfer learning in weld inspection image defect recognition is verified by fine-tuning the whole network by training the parameters of different layers using the frozen layer method. The effect of freezing different layers on the recognition performance of the model is also investigated.
Highlights
With the development of ray detection technology, inspection techniques with different imaging methods, such as radiographic inspection technology and ray real-time imaging inspection technology, have been more widely used
For some domestic heavy-equipment manufacturing enterprises, defect detection in large wall thickness of the welded parts still use radiographic film development technology, there is a waste of time in the use of film, pollution to the environment, low efficiency, and other issues; the use of AB-level or B-level blackness of the film can be very sensitive to detect the presence of tiny defects in the weld information, which cannot be replaced by real-time image detection technology [1–3]
In the process of manual evaluation of the film, first is to determine whether there are defects on the weld flaw detection image, determine the type of defects, followed by the determination of defect data, and the quality level assessment according to the quality acceptance criteria. is method is affected by the external conditions of radiation detection equipment and human subjective initiative, especially in strong light, which leads to eye fatigue, reduces the recognition ability of weld defects, and is easy to cause defect detection errors
Summary
With the development of ray detection technology, inspection techniques with different imaging methods, such as radiographic inspection technology and ray real-time imaging inspection technology, have been more widely used. E traditional film becomes a digital image obtained through a scanner, and in real-time imaging, analog signals is converted to digital signals, providing the possibility of computerized inspection and evaluation points On this basis, researchers have decomposed the process of manual film evaluation into computer processing processes, such as preprocessing of weld inspection images, segmentation of weld areas, extraction of defect features, defect classification and identification, and displaying and saving the final classification results [9]. Researchers have decomposed the process of manual film evaluation into computer processing processes, such as preprocessing of weld inspection images, segmentation of weld areas, extraction of defect features, defect classification and identification, and displaying and saving the final classification results [9] These systems are still inadequate in terms of full automation and cannot achieve complete automation and intelligent detection and require human-computer coupling in the weld defect detection and evaluation phase. In response to the aforementioned analysis, this paper attempts to apply deep learning to the field of weld inspection image recognition and evaluation and to achieve the goal of automatic identification of weld defects by establishing deep learning models for end-to-end training and learning of weld inspection images, so as to meet the requirements of modern enterprises for automation, intelligence, and efficiency in the field of weld inspection
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