Welding testing is particularly important in industrial systems, but there are still some deficiencies in terms of testing performance, anti-noise capability and defect identification in current mainstream welding non-destructive testing technologies. With the development of structured-light non-destructive testing technology, deep learning technology, signal processing technology and other fields, various possibilities have emerged that make it possible to propose new ideas for welding non-destructive testing. This study used a laser sensor to propose a non-destructive method for testing welding defects in seam contours. In order to solve the problems of low sampling rates and poor recognition accuracy in traditional methods of welding defect detection, the proposed method introduces image coding into laser sensors and applies deep-learning algorithms to the classification and detection of weld defect images. By preprocessing the weld seam by encoding one-dimensional data as two-dimensional images, this method develops a framework for the detection and classification of pre-coded laser weld seam images. After taking the original extracted weld image center trajectory data as one-dimensional sequence data, we utilized the method of encoding one-dimensional time series data as two-dimensional time-series images. In doing so, the one-dimensional laser data can be encoded into the corresponding two-dimensional images and, with the application of a deep neural network, welding defect classification and detection can be realized. Experimentation was used to verify that the proposed method is of higher accuracy than traditional methods for classifying and detecting defects directly from two-dimensional welding images.

The primary NDT method for welding defects is the image-based detection. Currently, the best performance for image-based detection is based on the transformer model. However, with its high accuracy, it has many limitations, such as large model parameters, large data sample requirements, and expensive computer resources. This model has a weaker ability to capture local features compared with global features. In this study, an improved and optimized welding defect detection and identification framework named Fast Multi-Path Vision transformer (FMPVit) is proposed based on the transformer model. This model uses a multilayer parallel architecture and enhances the local information capture ability of the model through advanced multiscale convolution feature aggregation and the addition of a new local convolution module. Finally, a validation test is carried out using an open dataset of weld seams. The model is proven to exhibit an evident performance improvement over the mainstream model baseline.

With the development of synthetic-aperture radar (SAR) image interpretation technology in recent years, classifying detected ships based on SAR images has become an important trend in ocean monitoring. However, owing to the underlying imaging mechanism, the texture of SAR images typically contains noise that cannot be easily eliminated. Therefore, in this study, we consider more stable target contour features to classify SAR images of marine vessels. Based on a deep learning algorithm, we propose a method to obtain contour bias features by employing style transfer learning to classify detected ships from SAR data. We also adopt transfer learning to improve the uneven distribution of SAR datasets of representative ships. The results of experiments conducted to evaluate the proposed approach show that contour bias features improve the generalization performance and classification accuracy of the model. They also show that transfer learning effectively avoids the problem of data imbalance and, thus, improves classification accuracy on the OpenSARShip 2.0 database.