# Welding Bead Inspection Using Image and Multi-Sensor Fusion
## 1 Introduction  
Welding is a production-based industry that is the basis for all industries, such as automobiles, construction, and shipbuilding. Among the examples in which welding is used include SRDs used in construction sites, as shown in Figure 1a,b. SRD is used to manage the shear force and shear stress of concrete structures and to improve the strength and safety of structures. In other words, welding work is very important, as SRD is directly related to safety, and inspections to check the quality of the product are essential. Therefore, when welding is performed through the machine, as shown in Figure 1c, inspection of the welding areas can be performed on the inspection table, as shown in Figure 1d. To this end, various welding quality inspection studies using sensors and image technologies have been published. At this time, the sensor inspection mainly performs the inspection using voltage, current, and gas amounts that determine the welding quality [1]. In the inspection method using sensors, various studies have been published, including a method of determining and inspecting sensor data measured in real time using a deep learning model and an inspection method of analyzing the waveforms of sensor data through deep learning [2; 3; 4; 5; 6; 7; 8]. In the inspection method using 2D images, methods of inspecting products for defects after dividing the welding in images taken with 2D vision cameras have been announced, and recently, methods of inspecting quality using KNN (K-nearest neighbor), K-means, improved Grabcut, etc., have been announced [9; 10; 11; 12; 13; 14; 15; 16; 17; 18; 19; 20; 21; 22; 23; 24; 25; 26; 27; 28]. Meanwhile, sensor inspection has the advantage of high accuracy, but it also has the disadvantage in that it is impossible to check the welding location. On the other hand, 2D image inspection can determine the location of the welding bead, but if there is noise or a measurement error in the image, its accuracy decreases, and some methods have difficulty in being slow due to high calculationcosts. Therefore, research is needed to solve the requirements of fast inspection time and high accuracy.  
In this paper, we proposed a method that combines sensor-based inspection using average current, average voltage, and mixed gas flow rate values with 2D image inspection to address these limitations. For image inspection, we employed an image projection-based method known for its high accuracy and fast inspection speed [16]. The proposed method offers the advantage of complementing the drawbacks of sensor and image inspection methods, enabling precise inspections. Furthermore, it improves the inspection speed by making a final determination of defects without proceeding to the image inspection stage in which the sensor inspection results classify the product as bad. The remainder of this paper was organized as follows: Section 2 introduces the conventional methods for inspecting welding beads using sensor and image data. Section 3 describes the algorithm proposed in this paper, and Section 4 presents the experimental results obtained using the proposed and existing algorithms. Finally, Section 5 presents the conclusions of this study.