## 4 Results of Performance Evaluation and Industrial Field Application
### Industrial Field Application  
This research developed an AI algorithm aimed at enhancing preprocessing and depth accuracy for ToF cameras. As a result, we were able to more easily perform noise removal and flattening of ToF depth data through AI learning, and we established a foundation for accuracy improvement, although only slight. However, it was evident that the ToF sensors still lack the millimeter-level accuracy and precision required for industrial applications. Through this research, we were able to enhance the accuracy and precision of ToF depth data using the AI algorithm, and conducted application evaluations using this data.  
Firstly, as shown in Figure 10, vision inspections using mobile robots are being conducted in industrial settings [20]. Since the accuracy and precision of mobile robots are limited to tens of centimeters, the precision of repeated positioning during image acquisition is low. To improve this, we used a 2D-based image matching process to correct the inspection images themselves. However, because the alignment rate does not reach 100% when this correction algorithm is applied, pseudo defects often occur. The most effective solution is to acquire images under the same conditions as the original data captured at the beginning. To achieve this, we used the ToF depth sensor (Boston Dynamics, Waltham, MA, USA) to calculate the positional error with depth data, and used this information to extract the robot correction coordinates, moving the robot arm to the position of the initially captured image to acquire the same image. This process, illustrated in Figure 10, was conducted using ToF depth data secured in this research.  
Figure 9: Depth accuracy comparison results when applying rule-based preprocessing and deep learning algorithm (10 times).  
As a result, as shown in Figure 11, the performance of the 3D data correction method using ToF depth was significantly improved compared to the conventional 2D image matching.  
Secondly, there are parts that are difficult to inspect with 2D images alone during vision inspections. If the color of the part intended to determine the presence or absence of assembly matches the background color, the accuracy of the inspection results is significantly reduced. To improve this, as illustrated in Figure 12, we applied a parallel algorithm that calculates the difference between 2D and 3D data using ToF depth information to enhance the accuracy of the inspections.  
In this paper, efforts to remove noise and enhance the accuracy of ToF sensors, which are relatively inexpensive and consume less power compared to Lidar, have proven that they are suitable sensors for various mobile platforms used in recent industrial settings. As research on performance improvement progresses, the value of their use increases,  
Figure 11: Difference from the original data before and after correction.  
Figure 10: Correction and high-precision image acquisition technology through point cloud registration.  
emphasizing that continuous research is needed to expand their application areas and to use them more effectively in industrial settings.