# Deep Learning-Based Classification of Weld Surface Defects
## 1 Introduction  
As a traditional processing method, the welding process is widely used in aerospace, machine design, energy generation, shipbuilding, petrochemical engineering, and other industries. Welding is a complicated process, which is often affected by the welding process and environmental uncertainties, and it is easy to produce welding defects such as overlap, pore, spatter, slag inclusion, and incomplete fusion. Therefore, it is necessary to conduct research on the detection and identification of weld surface defects. Common non-destructive testing methods for welds include ultrasonic testing [1], ray detection [2], and eddy current testing [3]. However, these methods have some limitations: (1) ray detection can easily result in side-effects on the human body; (2) ultrasonic testing is susceptible to the location, orientation, and shape of the defect; (3) eddy current testing is only applicable to conductive metal materials or non-metallic materials that can induce eddy currents. During the last decade, computer vision became an important field of artificial intelligence, which has been widely used in weld defect detection. It generally includes four steps: image acquisition, image preprocessing, feature extraction, and classification [4]. Feature extraction is one of the most critical technologies. Many studies have been carried out on feature extraction and classification of weld defects. [PERSON] et al. [5] isolated the defect features of thin-walled workpiece based on the background subtraction of Gaussian mixture model and constructed a decision tree to discern weld defects. [PERSON] et al. [6] extracted texture and geometric features of different weld defects and used support vector machine (SVM), k-nearest neighbor (KNN), and artificial neural network (ANN) tocomplete the classification. [PERSON] et al. [7] proposed a new method to automatically extract four geometric features from Lissajous figures and use machine learning-based classifiers to identify defects. [PERSON] et al. [8] obtained defect features by the exposure technique of double wall double image (DWDI) and used multi-layer perception (MLP) to continuously classify defect or no-defect. [PERSON] et al. [9] selected the shape and direction of weld defects as classification features, and proposed an adaptive network based fuzzy inference system to detect welding defects. [PERSON] et al. [10] extracted feature from the power density spectra (PDS) of the weld segmented areas and used ANN to match features in order to recognize different defects. In References [11; 12], a method based on principal component analysis (PCA) and SVM was proposed. It can effectively transform weld defects images to principal component space through PCA and complete the classification using SVM. However, the above feature extraction and classification methods only utilize the texture or geometric features of the weld defects images, and it is not ideal to form suitable features for classification. The history of deep learning originates from neuroscience experiments. As early as 1943, neurologist [PERSON] and mathematician [PERSON] constructed the first artificial neuron model according to the structure and working principle of biological neurons, which was called the \"MCP model\". Since then, many researchers have proposed various neural network models. However, due to the problem of gradient disappearance in the training method proposed at that time, it has been difficult to use this powerful model. This problem was not solved until the emergence of deep learning methods [13] in 2006. [PERSON] et al. [14] used AlexNet to make a breakthrough in ImageNet image classification competition, deep learning showed unprecedented development prospects. After that, researchers have proposed ZFNet, VGGNet, GoogLeNet, and ResNet models for large-scale image classification. Compared with the traditional feature extraction methods, these methods do not need to implement any features of the pre-selected image.