## 4 Detailed Proposed Technology and Test Results
### Industrial Field AI Algorithm Development Plan  
To improve the performance of deep learning algorithms, obtaining sufficient-quality training data is the most important aspect. However, obtaining sufficient learning data for developing vision AI algorithms within a short period that corresponds to the time of product production in industrial settings such as automobile assembly processes is extremely challenging. Since adequate data cannot be used when developing AI algorithms in industrial settings, a method must be developed to satisfy the algorithm's performance requirements using only a small amount of data that can be initially acquired. Acquiring learning data in industrial settings is very difficult, as mentioned earlier, and problems can arise due to the imbalance of the OK and NG data. The anomaly detection technique is being analyzed to solve this problem; however, it is still inadequate for implementation in industrial settings with diverse inspection images. In this study, we developed algorithms with various combinations of similar or different part images to improve the AI algorithm performance using a small amount of data, as shown in Figure 12. By selecting and using the inspection algorithm for parts with high accuracy among the developed algorithms,  
Figure 11: Comparison graph of learning accuracy for each representative network according to image augmentation error range of \(\pm\)5%, T-Matrix (auto), and engineer’s experience level. a high-performance detection algorithm was developed within a short period using only a small amount of learning data. To improve the algorithm detection performance, a large amount of learning data with diversity must be obtained. However, the best and easiest way to increase the diversity and accuracy of learning data is to mix between automobile parts to reduce the variance and bias, which is practically impossible to achieve under industrial settings. The Boosting technique is one of the methods that can be used to solve the generalization problem of machine learning algorithms. Boosting assigns more weight to misclassified samples from previous training results on the same data to predict more accurate results [23; 24]. However, Boosting has dependencies between data and network models, requiring various tuning operations to achieve the optimal performance. Conversely, the proposed learning method is very simple and intuitive, making it easy to use in industrial automation systems. Additionally, mixing similar parts of data can increase the generalization performance while reducing the variance and bias by increasing the diversity and quantity of the training data. This algorithm takes three primary inputs: the total number of car parts (\(N\)), an accuracy threshold (\(t\)) for effective model evaluation, and the number of iterations (\(I\)) to refine the models through repeated training. The output obtained is the trained models (\(M\)) for each car part that satisfy the accuracy threshold. The core process includes initializing a tracking table (\(T\)), selecting random subsets of parts for model training, comparing their accuracy against \(t\), and iterating until the most accurate algorithms for each part are identified. After randomly mixing inspection part images as shown in Algorithm 1 and training the model, only the parts with high inspection accuracy are used in the AI algorithm. For parts with low accuracy, the algorithm is retrained using a different mix of parts until the inspection accuracy is sufficiently high to be used in the AI algorithm. Using this method, we were able to improve the performance of the AI algorithm with minimal data and resources within a short period. Using the proposed AI algorithm development method, the results of training on the same data, same network model, and same parameter setting presents higher accuracy than training for a single part, as shown in Figure 13. In particular, applying the category technique to parts with an accuracy of 0.5 or less increased the accuracy to 0.9 or higher.