Surface defects inspection of titanium alloy by using YOLO algorithm

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.