## 4 Detailed Proposed Technology and Test Results
### Automation Technology for Maintaining the Performance of AI Algorithms  
4.1 Automatic Image Augmentation Technology That Accounts for Deviation in Mobile Robot's Shooting Position  
The image augmentation technique involves creating new data by appropriately transforming the original image during CNN deep learning training. It is effective in making the model robust when there is insufficient training data, and is important to improve the performance of deep learning algorithms [21, 22]. When acquiring images using a mobile robot in an automobile assembly process, the range of image acquisition deviations caused by robot position errors can be statistically calculated. Using the calculated image deviation range value, the image augmentation range can be specified during deep learning training. Thus, the image can be augmented within the same range as the image deviation that can occur due to the positional error of the mobile robot.  
Therefore, learning data with the same range of deviation as that of the inspection image can be additionally created, thereby improving the detection performance of the deep learning algorithm. Additionally, it is very effective at maintaining the algorithm performance when the error range changes due to environmental changes and robot deterioration since the algorithm can be automatically re-learned within the calculated deviation range for a certain period of time without the need for an engineer. The T-Matrix value of the image deviation information measured in the image error registration SW of Figure 1 is stored. The image acquisition deviation range for each robot position can be calculated as shown in Figure 9. It can be observed that the deviation of the error caused by the different position of the robot and the different pose of the arm for part shooting is different. Essentially, when training the deep learning algorithm using different robot position deviations for each part shooting position, an appropriate image augmentation value for each position can be used as shown in Figure 10. This method can prevent the degradation of the algorithm performance by learning with an image that is completely different from the inspection image during algorithm learning. Additionally, the error range analyzed by the image error registration software can be automatically parameterized for image augmentation without the intervention of an engineer.  
To verify the effectiveness of the image augmentation technique using T-Matrix values, we compared the results of learning with parameters set by engineers to the results of learning with parameters set automatically using the T-Matrix values. The results indicated  
Figure 10: Using the range of image deviation caused by robot position errors as image augmentation parameters.  
Figure 9: For each set of 100 images captured by the robot at each position, T-Matrix can be used to extract the range of augmentations.  
equivalent algorithm performance, as shown in Figure 11. Conversely, we observed that the algorithm's performance deteriorated when the image was augmented with a difference of \(\pm\)5% or more from the error range for each robot position. This implies that incorrect parameter settings by engineers during deep learning can degrade the algorithm performance, and there is a high possibility of algorithm performance deviation based on the engineer's ability. Additionally, maintenance costs can be minimized because the system can automatically learn the algorithm's performance despite environmental changes or robot deterioration, without requiring an engineer.