A Deep Learning System for Automated Weld Seam and Surface Defect Inspection
Resumo
This work presents a deep learning-based system for automated weld seam and surface defect inspection. A REST API with NestJS sends uploaded images to a secure FastAPI service running a YOLOv11 model, which returns predictions with class, confidence, and bounding boxes. The system delivers high accuracy, reduces human error, and supports deployment on various devices, including smartphones, for flexible industrial integration.Referências
Y.Wang and R. X. Gao, “Condition monitoring and fault diagnosis of welding processes: A review,” Journal of Manufacturing Processes, vol. 59, pp. 101–117, 2020.
D. Jung, J. Lee, and J. Kim, “Automated welding defect detection system using deep learning in manufacturing,” in 2018 IEEE International Conference on Industrial Technology (ICIT), 2018, pp. 819–824.
R. Zhao, R. Yan, Z. Chen, K. Mao, P. Wang, and R. X. Gao, “Deep learning and its applications to machine health monitoring,” Mechanical Systems and Signal Processing, vol. 115, pp. 213–237, 2019.
N. Jegham, C. Y. Koh, M. Abdelatti, and A. Hendawi, “Evaluating the evolution of yolo (you only look once) models: A comprehensive benchmark study of yolo11 and its predecessors,” arXiv preprint arXiv:2411.00201, 2024. [Online]. Available: [link]
X. Tao, Z. Zeng, J. Zeng, T. Zhao, and Q. Dai, “Pavement crack detection and identification based on improved yolov11,” International Journal of Cognitive Informatics and Natural Intelligence, vol. 18, no. 1, pp. 1–20, 2024.
M. Mazni, A. R. Husain, M. I. Shapiai, I. S. Ibrahim, R. Zulkifli, and D. W. Anggara, “Identification of concrete cracks using deep learning models: A systematic review,” Applications of Modelling and Simulation, vol. 1, no. 1, pp. 1–14, 2023. [Online]. Available: [link]
Q. Yuan, Y. Shi, and M. Li, “A review of computer vision-based crack detection methods in civil infrastructure: Progress and challenges,” Remote Sensing, vol. 16, no. 16, p. 2910, 2024.
Y. Zhang, W. Wu, J. Huang, and L. Cong, “Detection method of surface damage of concrete bridge based on improved yolov11,” in Proceedings of SPIE, vol. 13107, 2024, p. 131071K.
D. Jung, J. Lee, and J. Kim, “Automated welding defect detection system using deep learning in manufacturing,” in 2018 IEEE International Conference on Industrial Technology (ICIT), 2018, pp. 819–824.
R. Zhao, R. Yan, Z. Chen, K. Mao, P. Wang, and R. X. Gao, “Deep learning and its applications to machine health monitoring,” Mechanical Systems and Signal Processing, vol. 115, pp. 213–237, 2019.
N. Jegham, C. Y. Koh, M. Abdelatti, and A. Hendawi, “Evaluating the evolution of yolo (you only look once) models: A comprehensive benchmark study of yolo11 and its predecessors,” arXiv preprint arXiv:2411.00201, 2024. [Online]. Available: [link]
X. Tao, Z. Zeng, J. Zeng, T. Zhao, and Q. Dai, “Pavement crack detection and identification based on improved yolov11,” International Journal of Cognitive Informatics and Natural Intelligence, vol. 18, no. 1, pp. 1–20, 2024.
M. Mazni, A. R. Husain, M. I. Shapiai, I. S. Ibrahim, R. Zulkifli, and D. W. Anggara, “Identification of concrete cracks using deep learning models: A systematic review,” Applications of Modelling and Simulation, vol. 1, no. 1, pp. 1–14, 2023. [Online]. Available: [link]
Q. Yuan, Y. Shi, and M. Li, “A review of computer vision-based crack detection methods in civil infrastructure: Progress and challenges,” Remote Sensing, vol. 16, no. 16, p. 2910, 2024.
Y. Zhang, W. Wu, J. Huang, and L. Cong, “Detection method of surface damage of concrete bridge based on improved yolov11,” in Proceedings of SPIE, vol. 13107, 2024, p. 131071K.
Publicado
02/07/2025
Como Citar
NEVES, João A. G.; OLIVEIRA, Davidson Nunes de; MAIA, Helton.
A Deep Learning System for Automated Weld Seam and Surface Defect Inspection. In: ESCOLA REGIONAL DE ALTO DESEMPENHO DA REGIÃO NORDESTE (ERAD-NE), 6. , 2025, Natal/RN.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2025
.
p. 25-28.
DOI: https://doi.org/10.5753/erad-ne.2025.11766.
