# A Titanium Alloy Defect Detection Method Based on Optical-Acoustic Image Fusion
## 4 Practical Experiment
### Results and Analysis  
After the training using the above experimental conditions, the parameters of the Ti-YOLO model were 2.6 million, and the calculation amount was 7.3 billion, both of which are 3.7% and 13.1% lower than the basic YOLOv10n model. In addition, the detection accuracy of Ti-YOLO can reach 99.0% with a detection speed of more than 160 FPS. The training results are shown in Figures 15 and 16. The comparison between the test results and the actual results which have been bolded is shown in Table 5. It can be seen that optical detection can accurately determine the type of surface defect, which ensures that the absolute error is no more than 3 mm in the aspect of the defects' center position and 1 mm in the aspect of the defects' length. So, the optical detection system performed well within error permissibility. In the future, measures should be adopted to reduce the errors. Specifically, errors about the defects' position can mainly be caused by image rectification and image cropping. But errors can be reduced by improving the image processing program. In addition, errors about the defects' length can mainly be caused by the box selection of targets in the Ti-YOLO model. But errors can be reduced by optimizing the parameters of the algorithm. Acoustic detection imaging is shown in Figure 18. The plane rectangular coordinate system is established by taking the upper-left vertex of the image as the coordinate origin. It can be seen that the imaging inspection results show internal pore defects of different sizes, positions, and depths. After image post-processing, each defect is identified and labeled with a serial number. In addition, the information matrix was generated, and it is related to the detection image, which recorded the type, position, diameter, and buried depth of each defect in detail. The comparison between the real data and the test results which have been bolded is shown in Table 6. Obviously, the acoustic C-scan detection technology was efficient and accurate in judging the type of defect. In addition, the absolute error was no more than 1 mm in the aspect of the defects' diameter and the defects' buried depth, as well as no more than 2 mm in the aspect of the defects' center position. So, the acoustic detection system performed well within error permissibility. Figure 18: Acoustic detection imaging. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**No** & **Type** & **Position(mm)** & **Length(mm)** \\ \hline \multirow{2}{*}{1} & line defect & X = 241.19, Y = 114.32 & 10.82 \\  & **line defect** & **X = 240, Y = 115** & **10** \\ \hline \multirow{2}{*}{2} & line defect & X = 240.36, Y = 145.46 & 10.35 \\  & **line defect** & **X = 240, Y = 145** & **10** \\ \hline \multirow{2}{*}{3} & line defect & X = 239.17, Y = 177.40 & 10.50 \\  & **line defect** & **X = 240, Y = 175** & **10** \\ \hline absolute error & & \(|\delta|\leq 3\) mm & \(|\delta|\leq 1\) mm \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison between optical detection data and real data. The errors were mainly caused by the signal processing program. Specifically, the program cannot process the critical signals at the edge of defects completely. So, measures should be adopted to increase the performance of the acoustic detection imaging program. In addition, it is vital to use the motion control system with high precision and flexibility. As mentioned earlier in the article, the result from the information matrix can be generated via the optical surface defect detection task of the titanium alloy, which mainly includes the defect type, defect position, and defect length in detail. In addition, the result from the data matrix can be obtained by the acoustic internal defect detection task of the titanium alloy, which mainly includes the defect type, defect position, defect diameter, and defect buried depth.