## 4 Experimental Results and Analysis
### Testing and Evaluation
Figure 14: Measurement of the Standard Ball. (**a**) Measured Standard Ball; (**b**) 3D Point Cloud; (**c**,**d**) Areas of Captured Image Corresponding to Large Error Points. Figure 15: Prediction Results of the Standard Ball. (**a–d**) Prediction Results of Defective Cross-Section; (**e–h**) Prediction Results of Defect-Free Cross-Section. The quality of the stripes in Figure 14a is examined by the method proposed in this paper and in Ref. [19]. The corresponding defective points are found in the 3D point cloud of Figure 14b and removed, respectively, according to the two methods. The measurement error before and after processing is then calculated for each point in the point cloud according to Equation (2). The measurement errors before and after processing are shown in Table 7. It can be seen that the measurement error of the stripes is significantly reduced after being processed by the method proposed in this paper. Therefore, compared with the method of Ref. [19], the measurement error is smaller after the processing of the method in this paper. For the flat board, Figure 16a,b shows the captured image and 3D points measurement results of flat board. The large error points are mainly distributed at the edges of the board, caused by the asymmetry of the gray distribution of the stripes, as shown in Figure 16a. The stripe cross-sections of the 3D points corresponding to the 2D coordinates in the irregular region are input into the prediction algorithm, and the results are shown in Figure 14.0 According to the predicted results in Figure 17a,b, the structural light stripe cross-sections at the broken points at the edges of the flat board in Figure 16a are all severely defective and are evaluated as defective by the prediction algorithm. In contrast, according to the predicted results in Figure 17c,d, the structured light stripe cross-sections intercepted at the center of the flat board surface have a uniform grayscale distribution and are evaluated as defect-free by the prediction algorithm. Therefore, the results meet the established expectation. Similarly, the stripe cross-sections in the edge region of the flat board in Figure 16a are separately input into the prediction algorithm. The points corresponding to the cross-sections with large errors evaluated by the algorithm are removed from the point cloud map shown in Figure 16b. The measurement error information of the edges of the flat board before and after processing are summarized in Figure 14.1 The measurement error is defined as the relative distance of the points on the point cloud from the fitted plane. It can be seen that the measurement error for the flat board is also significantly reduced after processing by the method proposed in this paper. \begin{table}
\begin{tabular}{c c c} \hline
**Grayscale Distribution in** & **Measurement Error (mm)** & **Prediction Result** \\ \hline (a) & 1.009 & Defective \\ (b) & 1.097 & Defective \\ (c) & 0.914 & Defective \\ (d) & 0.988 & Defective \\ (e) & 0.015 & No Defects \\ (f) & 0.129 & No Defects \\ (g) & 0.094 & No Defects \\ (h) & 0.225 & No Defects \\ \hline \end{tabular}
\end{table} 
Table 6: Prediction Result Evaluation of Grayscale Distribution in Figure 15. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Unprocessed** & \
\begin{tabular}{c} **Processed by the Method in** \\ **This Paper** \\ \end{tabular} &
\begin{tabular}{c} **Processed by the** \\ **Method in Ref. [19]** \\ \end{tabular} \\ \hline Mean/mm & 0.670 & 0.163 & 0.326 \\ Std/mm & 0.786 & 0.106 & 0.264 \\ Max/mm & 1.836 & 0.328 & 1.009 \\ \hline \end{tabular}
\end{table} 
Table 7: Measurement Errors of the Standard Ball. Figure 16: Measurement of the Flat Board. (**a**) Measured Flat Board; (**b**) 3D Point Cloud. Figure 17: Prediction Results of the Flat Board. (**a**,**b**) Prediction Results of Defective Cross-Sections at the Broken Points at the Edges of the Flat Board; (**c**,**d**) Prediction Results of Defect-Free Cross-Section.