# NDT Method for Line Laser Welding Based on Deep Learning and One-Dimensional Time-Series Data
###### Abstract
The proposed method was divided into three stages: the preprocessing stage--image denoising, smoothing and unblurring to highlight the areas with pores; the feature extraction stage--segmentation of pores and the morphological/geometrical features that describe the porosity; and the intelligent classifier stage--definition, training, testing and validation of the random forest classifier [21]. Compared to traditional defect detection methods, these machine-learning methods have delivered better results for classification and detection. However, with the rapid development of deep learning in the past few years, deep-learning methods have surpassed machine-learning methods in classification and recognition accuracy. At present, the development of deep-learning technology is gradually becoming mature. Deep learning has shown better performance in welding image classification than traditional machine learning for the training of a large number of data samples. A variety of methods adopting deep-learning neural networks for welding defect detection have also been proposed. For example, [PERSON] et al. proposed a method based on a convolutional neural network (CNN) that uses a single RGB camera to inspect welding defects on the transmission surface of the engine. The proposed method consists of two steps, beginning with extraction of the welding area to be inspected from the captured image. In this first step, to extract the welding area from the captured image, a CNN-based approach is used to detect the center of the engine transmission in the image. In the second stage, the extracted area is identified by another CNN as defective or non-defective [22]. [PERSON] et al. designed an 11-layer CNN classification model based on weld images to identify weld penetration defects. The CNN model has made full use of arc lights by combining them in various ways to form the complementary features. Test results show that the CNN model has better performance than our previous work [23]. Industrial X-ray weld image defect detection is an important research field for non-destructive testing (NDT) [24]. [PERSON] et al. proposed a multitask deep one-class CNN for defect classification. They built a stacked encoder-decoder autoencoder to learn feature representation from normal images. The encoder is used as a feature extractor based on the hard sharing scheme for multitask learning. For defect detection, their approach achieved results almost as good as the supervised method, even without any annotated data [25]. [PERSON] et al. focused on establishing an end-to-end automatic detection model for X-ray welding defects to improve the accuracy and efficiency of detection based on a deep learning algorithm. Considering the feature information of welding defects, this study achieved improvements on the basis of the Faster R-CNN method and used the deep residual network Res2 Net to improve the feature extraction ability [26]. In the above-mentioned methods, most of the welding datasets used were composed of X-ray images and welding images collected by a CCD camera. These types of welding images have certain limitations. When the X-ray image and the CCD camera collect the welding image for welding defect classification processing, noise is produced and the accuracy of the classification affected. Various researchers have proposed several noise reduction methods for processing image noise, including skeleton extraction, morphological opening and closing operations, filters and other noise reduction processing methods, but the processing effect is not good enough and processed noise still exists. Furthermore, the X-ray image and the image collected by the CCD camera are not sensitive to welding surface defects, such as depressions and burrs, and the radiation produced when the X-ray image is collected is a problem that needs to be considered.