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Detection and Recognition of the Seam for Gap-Free Butt Joints Based on Scheimpflug Imaging and Deep Learning Cover

Detection and Recognition of the Seam for Gap-Free Butt Joints Based on Scheimpflug Imaging and Deep Learning

By: Jiaxin Liu,  Banghe Su and  Jinqiang Gao  
Open Access
|Mar 2026

References

  1. Pham, D.-A., Bui, D.-Q., Le, T.-D., Tran, D.-H., Nguyen, T.-H.: Automatic welding seam tracking and real-world coordinates identification with machine learning method. Results in Engineering 23 (2024), 10.
  2. Shang, G., Xu, L., Li, Z., Zhou, Z., Xu, Z.: Digital-twin-based predictive compensation control strategy for seam tracking in steel sheets welding of large cruise ships. Robotics and Computer-Integrated Manufacturing 88 (2024), 16.
  3. Rout, A., Deepak, B.B.V.L., Biswal, B.B.: Advances in weld seam tracking techniques for robotic welding: A review. Robotics and Computer-Integrated Manufacturing 56 (2019), 12-37.
  4. Lin, J., Jia, A., Huang, W., Wen, Z., Hong, B., Hong, Y.: Weld seam tracking method of root pass welding with variable gap based on magnetically controlled arc sensor. The International Journal of Advanced Manufacturing Technology 126 (2023), 5227-5243.
  5. Xu, F., He, L., Hou, Z., Xiao, R., Zuo, T., Li, J., Xu, Y., Zhang, H.: An automatic feature point extraction method based on laser vision for robotic multi-layer multi-pass weld seam tracking. The International Journal of Advanced Manufacturing Technology 131 (2024), 5941-5960.
  6. Wenji, L., Zhenyu, G., Xiao, J., Li, L., Jianfeng, Y.: Research on the seam tracking of narrow gap P-GMAW based on arc sound sensing. Sensors and Actuators A: Physical 292 (2019), 205-216.
  7. Górka, J., Jamrozik, W.: Enhancement of Imperfection Detection Capabilities in TIG Welding of the Infrared Monitoring System. Metals 11 (2021), 1624.
  8. Górka, J., Jamrozik, W., Wyględacz, B., Kiel-Jamrozik, M., Ferreira, B.G.: Virtual Sensor for On-Line Hardness Assessment in TIG Welding of Inconel 600 Alloy Thin Plates. Sensors 24 (2024), 3569.
  9. Jamrozik, W., Górka, J.: Assessing MMA Welding Process Stability Using Machine Vision-Based Arc Features Tracking System. Sensors 21 (2020), 84.
  10. Guo, Q., Yang, Z., Xu, J., Jiang, Y., Wang, W., Liu, Z., Zhao, W., Sun, Y.: Progress, challenges and trends on vision sensing technologies in automatic/intelligent robotic welding: State-of-the-art review. Robotics and Computer-Integrated Manufacturing 89 (2024), 45.
  11. Xu, F., Xu, Y., Zhang, H., Chen, S.: Application of sensing technology in intelligent robotic arc welding: A review. Journal of Manufacturing Processes 79 (2022), 854-880.
  12. Nguyen, A.H., Ly, K.L., Lam, V.K., Wang, Z.Y.: Generalized Fringe-to-Phase Framework for Single-Shot 3D Reconstruction Integrating Structured Light with Deep Learning. Sensors 23 (2023), 18.
  13. Yang, L., Deng, J., Shen, J.: A new passive vision weld seam tracking method for FSW based on K-means. The International Journal of Advanced Manufacturing Technology 128 (2023), 3283-3295.
  14. Sun, C., Liu, H.B., Jia, M.N., Chen, S.Y.: Review of Calibration Methods for Scheimpflug Camera. Journal of Sensors 2018 (2018), 15.
  15. Louhichi, H., Fournel, T., Lavest, J.M., Aissia, H.B.: Self-calibration of Scheimpflug cameras: an easy protocol. Measurement Science and Technology 18 (2007), 2616-2622.
  16. Zhang, X., Zhou, T.: Generic Scheimpflug camera model and its calibration. IEEE International Conference on Robotics and Biomimetics (ROBIO), Zhuhai, Peoples R China, 2015, 2264-2270.
  17. Cornic, P., Illoul, C., Cheminet, A., Le Besnerais, G., Champagnat, F., Le Sant, Y., Leclaire, B.: Another look at volume self-calibration: calibration and self-calibration within a pinhole model of Scheimpflug cameras. Measurement Science and Technology 27 (2016), 15.
  18. Girshick, R., Donahue, J., Darrell, T., Malik, J.: Rich feature hierarchies for accurate object detection and semantic segmentation. Proceedings of the IEEE conference on computer vision and pattern recognition, 2014, 580-587.
  19. Girshick, R.: Fast r-cnn. Proceedings of the IEEE international conference on computer vision, 2015, 1440-1448.
  20. Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: Towards real-time object detection with region proposal networks. IEEE transactions on pattern analysis and machine intelligence 39 (2016), 1137-1149.
  21. Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You only look once: Unified, real-time object detection. Proceedings of the IEEE conference on computer vision and pattern recognition, 2016, 779-788.
  22. Redmon, J., Farhadi, A.: YOLO9000: better, faster, stronger. Proceedings of the IEEE conference on computer vision and pattern recognition, 2017, 7263-7271.
  23. Hussain, M.: YOLO-v1 to YOLO-v8, the Rise of YOLO and Its Complementary Nature toward Digital Manufacturing and Industrial Defect Detection. Machines 11 (2023), 25.
  24. Lin, T.-Y., Dollár, P., Girshick, R., He, K., Hariharan, B., Belongie, S.: Feature pyramid networks for object detection. Proceedings of the IEEE conference on computer vision and pattern recognition, 2017, 2117-2125.
  25. Yang, L., Fan, J., Liu, Y., Li, E., Peng, J., Liang, Z.: Automatic Detection and Location of Weld Beads With Deep Convolutional Neural Networks. IEEE Transactions on Instrumentation and Measurement 70 (2021), 1-12.
  26. Li, S., Gao, J., Zhou, E., Pan, Q., Wang, X.: Deep learning-based fusion hole state recognition and width extraction for thin plate TIG welding. Welding in the World 66 (2022), 1329-1347.
  27. Song, L., Kang, J., Zhang, Q., Wang, S.: A weld feature points detection method based on improved YOLO for welding robots in strong noise environment. Signal, Image and Video Processing 17 (2022), 1801-1809.
  28. Dai, W., Li, D., Tang, D., Jiang, Q., Wang, D., Wang, H., Peng, Y.: Deep learning assisted vision inspection of resistance spot welds. Journal of Manufacturing Processes 62 (2021), 262-274.
  29. Deng, H., Liu, Y., Zhang, G., Qu, X., Ge, L., Yang, S.: Multi-angle Scheimpflug projection 3D microscope: Design, calibration, and three-dimensional reconstruction. Measurement 222 (2023), 22.
  30. Cai, K.W., Miao, X.Y., Wang, W., Pang, H.S., Liu, Y., Song, J.Y.: A modified YOLOv3 model for fish detection based on MobileNetv1 as backbone. Aquacultural Engineering 91 (2020), 9.
DOI: https://doi.org/10.2478/adms-2026-0001 | Journal eISSN: 2083-4799 | Journal ISSN: 1730-2439
Language: English
Page range: 5 - 21
Submitted on: Feb 4, 2026
Accepted on: Mar 19, 2026
Published on: Mar 31, 2026
In partnership with: Paradigm Publishing Services
Publication frequency: 4 issues per year

© 2026 Jiaxin Liu, Banghe Su, Jinqiang Gao, published by Gdansk University of Technology
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.