## 4 Experiments
### Image Feature Extraction  
#### 4.2.1 Effect Analysis of Target Tracking  
Taking a vibration source image as the object, the features were extracted by the SURF (Figure 9) and expressed as center point error (vector 1) and success rate (vector 2). The center point error refers to the Euclidean distance between the center of the target frame and the real target frame. The mean error of the centers is the sum of center errors divided by the total number of frames. If more than 50% of the target frame and the real target frame is overlapped, the frame is considered as being correctly tracked. The success rate stands for the ratio of correctly tracked frames to the total number of frames.  
After SURF extraction, a total of 4096 vectors were obtained:  
* Vector1\({}_{(4096)}\) = [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 2.453, 0.0, 5.042, 0.0, 1.899,..., 0.0, 0.0, 0.0, 0.6, 2.869, 0.0, 0.0, 0.0, 0.0, 0.0];
* Vector2\({}_{(4096)}\) = [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.485, 0.0, 0.941, 4.506, 2.171,..., 0.0, 0.0, 0.0, 0.0, 0.57, 2.997, 0.0, 0.0, 0.0, 0.0, 0.0];  
Table 4 compares the results of the proposed integrated extraction method with those of several traditional feature extraction methods.  
It can be seen that the integrated extraction method had a better effect than the traditional extraction methods for texture, shape and intrinsic features, respectively. In terms of mean center error, the proposed method surpassed the shape extraction method by 8%, the texture extraction method by 7% and the intrinsic extraction method by 4%. In terms of success rate, the proposed method outperformed the shape extraction method by 1.7%, the texture extraction method by 1.4% and the intrinsic extraction method by 1%.  
In this paper, the effect of the integrated extraction method hinges on the modification of the original image according to the points detected by the SURF. Therefore, repeated experiments were conducted using the modified weights, and the results were plotted as Figure 10.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**No.**} & \multirow{2}{*}{**Name**} & **Hammer** & **Hammer** & **Hammer** & **Hammer** & **Hammer** \\  & & **No. 1** & **No. 2** & **No. 3** & **No. 4** & **No. 5** \\ \hline
1 & GoogLeNet & 25.3 (90.1) & 24.3 (90.6) & 25.1 (89.9) & 25.8 (91.1) & 25.0 (90.4) \\
2 & SURF & 26.7 (89.7) & 26.9 (89.9) & 26.3 (89.1) & 26.1 (90.4) & 25.1 (90.8) \\
3 & Target contour & 27.4 (89.3) & 27.8 (88.1) & 27.2 (87.2) & 28.9 (88.9) & 27.9 (89.1) \\
4 & Multiply-features & 24.5 (91.1) & 24.1 (92.3) & 24.7 (90.7) & 24.9 (93.6) & 23.9 (91.5) \\ \hline \hline \end{tabular} Note: the format in the table is: error (rate).  
\end{table}
Table 4: Mean error of the center points (pixel) and the success rate (%).  
Figure 9: Comparison of two indexes under different weight.  
As shown in Figure 10, with the increase of weight, the success rate remained essentially the same, while the center error exhibited a decreasing trend. This means the best grayscale ratio between the defect point and the original image is 3:7. Overall, the integrated extraction method did better than the signal method, and the effect of GoogLeNet was improved by superimposing SURF feature points onto the original image.