Surface defects inspection of titanium alloy by using YOLO algorithm

Yang Zhao; Yunxuan Zou; Mingzhen Wang; Pinghua Yang

doi:10.1117/12.3049481

8 November 2024 Surface defects inspection of titanium alloy by using YOLO algorithm

Yang Zhao, Yunxuan Zou, Mingzhen Wang, Pinghua Yang

Proceedings Volume 13416, Fourth International Conference on Advanced Algorithms and Neural Networks (AANN 2024); 1341636 (2024) https://doi.org/10.1117/12.3049481
Event: 2024 4th International Conference on Advanced Algorithms and Neural Networks, 2024, Qingdao, China

Abstract

Titanium alloys are extensively utilized in the aerospace industry due to their exceptional mechanical properties and resistance to corrosion. The presence of surface defects in titanium alloys can significantly impact their performance. This paper investigates automatic classification and detection methods for surface defects of titanium alloy based on the YOLOv8 algorithm in deep learning. An optical detection system was designed for getting high-resolution images of the flaws information in titanium alloy samples, which were used to establish the data set of defect recognition. It was found that the proposed method can effectively achieve real-time detection of surface defects in titanium alloy.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Yang Zhao, Yunxuan Zou, Mingzhen Wang, and Pinghua Yang "Surface defects inspection of titanium alloy by using YOLO algorithm", Proc. SPIE 13416, Fourth International Conference on Advanced Algorithms and Neural Networks (AANN 2024), 1341636 (8 November 2024); https://doi.org/10.1117/12.3049481

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