# Automatic Detection of Welding Defects Using Faster R-CNN
## 1 Introduction  
The welding process accounts for more than 60% of the entire process in the shipbuilding and offshore sector [1]. For the weld testing, there are various technologies such as radiographic testing (RT), ultrasonic testing (UT) and magnetic testing (MT) used as non-destructive testing (NDT). Of them, ship owners particularly prefer RT whose results can be stored permanently and that can visually check the inside of the weld of all materials to other types of NDT.  
Currently, technicians directly perform welding testing on structures of 500 blocks or more to inspect the welding process in domestic and overseas shipyards. Since welding information of more than 2000 locations per block is manually prepared, omissions and errors commonly occur, which requires additional work, resulting in a huge amount of time and cost. To derive a consistent and rational result from testing that is manually conducted, there is a need for an automation and objectification system of testing that improves inspectors' understanding.  
Studies on automatic detection of the welding defects have been long conducted. Of them, ref. [2] used image pre-processing as a method of extracting features, and classified the type of welding defects using a neural network or Support Vector Machine (SVM) [3] for automatic identification. The form of the neural network used here is multi-layer perceptron (MLP) that is used only for classifying defects. Hence, it is not regarded as a neural network that classifies and reads images. Of various deep learning algorithms, a convolutional neural network (CNN) that has recently been researched a lot for image classification shows high performance compared to conventional algorithms. With regard toobject detection, ref. [4] used CNN that achieved higher performance than previously used HOG [5] or SIFT [6]. In [7] the boundaries of defects were classified based on image feature map extracted from the neural network using CNN and SVM, but not only the boundaries of defects but also the types of defects are important because NDT rules depending on the type of defect. Among the methods of object detection using neural networks, Fast R-CNN [8] and Faster R-CNN [9] are mainly used for object detection as they can classify the types and locations of objects in one network. In the medical sector that handles radiographic images, research on object detection employing CNN has already been underway. CNN detected features different to normal ones in chest radiographic images [10], and a study was conducted to detect nerve regions using CNN in ultrasound images [11]. The performance of detecting objects in radiographic images can be confirmed in several studies, and the possibility of detecting defects in radiographic images using CNN was confirmed.  
In this paper, we propose an algorithm that automatically detects the welding defects in radiographic images by employing Faster R-CNN that shows high-performance in terms of accuracy. We compared ResNet [12] and Inception-ResNet V2 [13] that showed high-performance in ImageNet by configuring them as backbone networks. We conducted an experiment by analyzing the anchor size in Faster R-CNN in a form suitable for defects. By taking into account the limited number of data, we improved the accuracy of the proposed algorithm using data augmentation. 1 Table 1 describes the method and features of defect detection.