H. P. Birari et al., “Advancements in Machine Vision for Automated Inspection of Assembly Parts: A Comprehensive Review,” International Research Journal on Advanced Science Hub, vol. 5, no. 10, pp. 365-371, 2023. DOI:10.47392/IRJASH.2023.065

Z. Zheng et al., “Surface Defect Detection of Electronic Components Based on FaSB R-CNN,” in Proceedings of the International Conference on Cognitive Systems and Signal Processing, Singapore: Springer Nature, pp. 555-569, 2022. DOI:10.1007/978-981-99-0617-8_40

Y. Sun, Y. Cai, Y. Li and Y. Zhao, “Defect Detection of Production Surface Based on CNN,” in Green, Pervasive, and Cloud Computing: 15th International Conference, GPC 2020, Xi'an, China, November 13–15, 2020, Proceedings, Springer International Publishing, pp. 405-412, 2020. DOI:10.1007/978-3-030-64243-3_30

G. Son and B. Shin, “A Study on Weld Defects Classification Using CNN to Enhance Non-destructive Testing,” Journal of the Korean Society for Next Generation Computing, vol.20, no.2, pp. 30-38, 2024. DOI:10.23019/kingpc.20.2.202404.003

D. H. Kim, “Implementation of an AI Deep Learning-based Inspection System for Detecting Defective Vehicle Action Loaders,” The Transactions of the Korean Institute of Electrical Engineers, vol. 72, no. 12, pp. 1714-1721, 2023.

P. Yedamale, “Brushless DC (BLDC) Motor Fundamentals,” Microchip Technology Inc., pp. 3-15, 2003.

C. Shorten and T. M. Khoshgoftaar, “A survey on image data augmentation for deep learning,” Journal of Big Data, vol. 6, no. 1, pp. 1-48, 2019. DOI:10.1109/TKDE.2009.191

S. J. Pan and Q. Yang, “A survey on transfer learning,” IEEE Transactions on Knowledge and Data Engineering, vol. 22, no. 10, pp. 1345-1359, 2010. DOI:10.1109/TKDE.2009.191

MathWorks, “Convolutional Neural Network,” 2024. [Online]. Available: https://kr.mathworks.com/discovery/convolutional- neural-network.html. [Accessed: 16-Oct-2024].

K. Simonyan and A. Zisserman, “Very deep convolutional networks for large-scale image recognition,” arXiv preprint arXiv:1409.1556, 2014. DOI:10.48550/arXiv.1409.1556

C. Szegedy et al., “Rethinking the inception architecture for computer vision,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2818-2826, 2016. DOI:10.1109/CVPR.2016.308

F. Chollet, “Xception: Deep learning with depthwise separable convolutions,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1251-1258, 2017. DOI:10.1109/CVPR.2017.195

T. G. Dietterich, “Ensemble methods in machine learning,” in Proceedings of the International Workshop on Multiple Classifier Systems, pp. 1-15, 2000. DOI:10.1007/3-540-45014-9_1

M. U. Salur and İ. Aydın, “A soft voting ensemble learning-based approach for multimodal sentiment analysis,” Neural Computing and Applications, vol. 34, pp. 18391-18406, 2022. DOI:doi.org/10.1007/s00521-022-07451-7

Chen, T. & Guestrin, C., “XGboost: A scalable tree boosting system,” In Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining, pp. 785-794, 2016 DOI:10.1145/2939672.2939785