# Recognition of Manual Welding Positions from Depth Hole Image Remotely Sensed by RGB-D Camera
## 1 Introduction  
Interest in establishing an accurate ship production plan to observe delivery dates, reduce costs, and improve productivity is continuously increasing in the shipbuilding industry. Since man-hours is one of the major factors in ship production planning, it is important to predict the required man-hours as early and accurately as possible. As illustrated in Figure 1, the shipbuilding process consists primarily of design, fabrication, assembly, outfitting, painting, creation, and sea trial. The most important role in the assembly process is played by welding used in assembly, outfitting, and erection [1]. The welding man-hour accounts for a significant portion of total shipbuilding man-hours, thus the emphasis on factors affecting the welding man-hour is unavoidably high. As a result, among the various factors influencing welding man-hours, the influence of working posture should be noted. Many production facilities in shipyards are now being automated, as are welding operations. However, as the assembly stage progresses from sub-assembly to grand-assembly stage, it becomes practically difficult to apply automation to block assembly. Many block structures are not suitable for automated equipment to input, increasing the manual welding rate. Due to the diverse and complex working space within the block, manual welding requires various postures of the welder. The difficulty of each posture is different; therefore, the difference in working time according to the posture occurs [2, 3, 4]. When the workload during welding is high and the proportion of difficultpostures increases, so does the working time, increasing total man-hours. As a result, to create a production plan that accurately reflects the progress in assembly shops, it is necessary to identify the working posture that affects the man-hour early in the planning process and manage the plan so that the work can be completed efficiently. In manual welding, the welder takes various postures according to the angle and position required to weld the object to the position indicated by the drawing. Manual welding is performed in the current assembly process in four positions: flat, horizontal, vertical, and overhead. The welder normally can check the assembly sequence and welding position from the drawings so that the welding position to be taken can be roughly estimated in advance. However, because the assembly sequence in the drawing may differ from the actual working shop for work efficiency, welding is frequently performed in an arbitrary position different from predicted. Such an arbitrary position in the actual field must also be recognized. Previous attempts to analyze the difference in man-hours based on welding position have been made, but it is difficult to find a study that recognizes an arbitrary position and analyzes the effect of the working posture. As a result, the majority of research has focused on recognizing human motions or working postures. A difference from the work performed in other industrial fields is that a peculiarly external factor appears in welding. The extremely bright light of the welding arc always causes a visual disturbance, making it difficult to recognize the posture. This phenomenon is an unavoidable obstacle when using any image processing equipment, including RGB-D, and must be overcome for correct posture recognition. In this study, when shooting with an RGB-D camera, we propose a method to remove the obstructive elements from the depth image and restore the damaged image close to the original working posture. The goal is to determine the working position in real-time by detecting the welder's posture without contact with welders and without space restrictions. The proposed method searches for the area where the welding arc damages the depth information in the depth image using the image moment. The depth hole filling technique then restores the lost depth information.