A Titanium Alloy Defect Detection Method Based on Optical–Acoustic Image Fusion
Source abstract
Abstract
Nowadays, a single detection method is insufficient for comprehensively and clearly identifying both surface defects and inner defects in titanium alloys. To address this limitation, this paper proposes a titanium alloy defect detection method based on optical–acoustic image fusion. A detection system was developed to achieve comprehensive and precise inspection of titanium alloys by integrating advanced deep learning-based optical testing technology, reliable C-scan ultrasonic detection technology, and information fusion techniques. Furthermore, the PC software can output interactive fusion results and generate decision-level detection reports. The experimental results demonstrate that the surface defect detection algorithm achieves an accuracy of 99.0%, with a surface defect size measurement resolution of 0.01 mm, an internal defect size measurement resolution of 1 mm, and a positional error within 2 mm. It was found that the proposed method provides a potential solution for the practical application of inspecting surface defects and inner defects in the materials.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Industrial Vision Systems and Defect Detection · Non-Destructive Testing Techniques · Welding Techniques and Residual Stresses
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Information Science and Engineering, Harbin Institute of Technology (Weihai), Weihai 264209, China
Funding
Natural Science Foundation of China · Shandong Provincial Natural Science Foundation · major scientific and technological innovation projects in Shandong Province
References (30)
Show source-provided references
1. Research and development progress of titanium alloy materials in recent five years
2. Research front and trend of specific laser additive manufacturing techniques
3. An overview of target detection algorithms based on deep learning
4. An efficient re-parameterization feature pyramid network on YOLOv8 to the detection of steel surface defect10.1016/j.neucom.2024.128775 ↗
5. Rail defect detection method based on improved YOLOv7
6. Li, J.W., and Chen, J.M. (2001). Nondestructive Testing Manua, China Machine Press. [1st ed.].
7. Laser ultrasonic nondestructive testing of small defects in metals using multi-channel fiber interference
8. A comprehensive structural analysis process for failure assessment in aircraft lap-joint mimics using intramodal fusion of eddy current data10.1080/09349847.2012.660242 ↗
9. milliTRACE-IR: Contact tracing and temperature screening via mmWave and infrared sensing10.1109/JSTSP.2021.3138632 ↗
10. Target detection and tracking of smoke obscured driverless vehicles based on fusion of infrared camera and millimeter wave radar
11. Nondestructive testing method of metal material defects based on multi-model signals
12. Redmon, J., Divvala, S., and Girshick, R. (2016, January 12). You only look once: Unified real-time object detection. Proceedings of the Computer Vision and Pattern Recognition, Las Vegas, NV, USA.10.1109/CVPR.2016.91 ↗
13. YOLO9000: Better, Faster, Stronger
14. Redmon, J., and Farhadi, A. (2018). YOLOv3: An incremental improvement. arXiv.
15. Bochkovskiy, A., and Wang, C.Y. (2020). YOLOv4: Optimal speed and accuracy of object detection. arXiv.
16. Li, C.Y., and Jia, H.L. (2022). YOLOv6: A single-stage object detection framework for industrial applications. arXiv.
17. Wang, C.Y., Bochkovskiy, A., and Liao, H.Y.M. (2022). YOLOv7: Trainable bag-of-freebies sets new state of the art for real-time object detectors. arXiv.10.1109/CVPR52729.2023.00721 ↗
18. Wang, C.Y., Ye, H., and Liao, H.Y.M. (October, January 29). YOLOv9: Learning what you want to learn using programmable gradient information. Proceedings of the European Conference on Computer Vision (ECCV), Milan, Italy.
19. Wang, A. (2024). YOLOv10: Real-time end-to-end object detection. arXiv.
20. Optical element surface roughness measurement system based on image preprocessing
21. Design of preprocessing module for SAR image target contour enhancement
22. Review of industrial metal surface defect detection based on computer vision
23. Infrared target detection algorithm based on improved Faster R-CNN10.5768/JAO202445.0202001 ↗
24. K Asymmetric geometric correction network for document images10.11834/jig.220426 ↗
25. Liu, T. (2023). Research on Multi-Object Tracking Technology Based on Infrared and Radar Fusion. [Master’s Thesis, Harbin Institute of Technology].
26. Pedestrian detection at night based on fusion of infrared camera and millimeter wave radar
27. Kong, S., Gan, L., and Wang, R. (2022, January 19–21). Target tracking algorithm of radar and infrared sensor based on multi-source information fusion. Proceedings of the 2022 International Conference on Artificial Intelligence, Information Processing and Cloud Computing (AIIPCC), Kunming, China.10.1109/AIIPCC57291.2022.00088 ↗
28. Multimodal soft jumping robot with self-decision ability10.1088/1361-665X/ac102d ↗
29. Google DeepMind’s gemini AI versus ChatGPT: A comparative analysis in ophthalmology10.1038/s41433-024-02958-w ↗
30. Sandler, M., Howard, A., and Zhu, M. (2018). MobileNetV2: Inverted residuals and Linear Bottlenecks. arXiv.10.1109/CVPR.2018.00474 ↗