# Recognition of Manual Welding Positions from Depth Hole Image Remotely Sensed by RGB-D Camera
## 5 Conclusions  
Since frequent manual welding in the shipbuilding assembly process produces different man-hours depending on the working posture, a technique capable of measuring and analyzing the working posture in advance is required. In this study, a method for recognizing working postures using RGB-D cameras was proposed. A method for resolving the problem of difficult welding position recognition due to the welding arc was presented. We created an algorithm that searches for depth holes in two-dimensional depth images and removes them using the image moment and the image inpainting technique. Two-dimensional coordinates of body joints were extracted from depth images and combined with depth information to create three-dimensional joints using open-source software. Those joints were used to establish a standard for welding positions, and the working postures were recognized and classified based on the standard. The method proposed in this study consists of (1) excluding direct contact or interference with field workers, (2) acquiring data through remote sensing based on an RGB-D camera, (3) recognizing posture using a pre-trained CNN model, and (4) determining the working position in real-time. Thus, the proposed method could provide an efficient tool for measuring and recognizing the working posture in the field using an inexpensive depth camera, by automatically eliminating the disturbance factors of the image. Instead of removing all depth holes from the depth image, we were able to obtain a performance improvement of more than 40% by removing only the necessary parts. The coordinates of joint extracted from the restored image and those of the ground truth showed a similar tendency overall. The error of the hand and neck joints covered by the depth hole was large, but the correlation between the joints did not change significantly. As a result of classifying the working position by learning the preprocessed depth image on the CNN model, high accuracy of 98.72% was obtained. This shows 13% points higher performance than the 85.83% accuracy of the depth image without preprocessing. The proposed method can be used in shipbuilding and other industries that require a variety of working postures. Analyzing working postures with high workloads can help prevent accidents and musculoskeletal disorders in workers. Assume, for example, that the recognized welding position is reflected in the formula used to calculate welding man-hours. In that case, it can assist the shipyard in calculating realistic man-hours and thus managing an efficient production schedule. Interference factors caused by bright light may occur in the field in the electrical, electronic, and optical industries. Furthermore, because construction and sports happen outside, motion capture accuracy may suffer. Thus, the findings of this study are expected to help detect external disturbances or recognize work postures in other industries where accurate depth images are impossible to obtain due to potential disturbances. Figure 22: Examples of abnormal depth holes: (**a**) vertical, (**b**) horizontal welding positions, and (**c**) a case that covers whole arm and upper body. **Author Contributions:** Conceptualization, J.-H.N. ; methodology, J.-H.K. and J.-H.N. ; software, J.-H.K. ; validation, J.-H.K. ; formal analysis, J.-H.K. and J.-H.N. ; investigation, J.-H.N. ; resources, J.-H.K. ; data curation, J.-H.K. ; writing--original draft preparation, J.-H.K. and J.-H.N. ; writing--review and editing, J.-H.N. ; visualization, J.-H.K. ; supervision, J.-H.N. ; project administration, J.-H.N. ; funding acquisition, J.-H.N. All authors have read and agreed to the published version of the manuscript. **Funding:** This research was supported in part by the project \"Development of master data system of lead time for precision enhancement of shipbuilding production management (project ID: 20000208),\" sponsored by the Ministry of Trade, Industry, and Energy of Korea. **Institutional Review Board Statement:** Not applicable.