# Adaptive Multi-View Image Mosaic Method for Conveyor Belt Surface Fault Online Detection
## 4 Experiments and Results
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Parameters** & **Figure 13(a5)** & **Figure 13(a6)** & **Figure 13(a7)** & **Figure 13(a8)** & **Figure 13(b5)** & **Figure 13(b6)** & **Figure 13(b7)** & **Figure 13(b8)** \\ \hline Quantity (piece) & 232 & 152 & 16 & 15 & 175 & 122 & 7 & 22 \\ Time (ms) & 578 & 234 & 8 & 8 & 546 & 203 & 8 & 8 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of image feature extraction when the threshold value of Hessian matrix determinant response value. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Threshold** & **Parameters** & **Figure 13(a5)** & **Figure 13(a6)** & **Figure 13(a7)** & **Figure 13(a8)** & **Figure 13(b5)** & **Figure 13(b6)** & **Figure 13(b7)** & **Figure 13(b8)** \\ \hline \multirow{3}{*}{500} & Quantity (piece) & 694 & 373 & 27 & 35 & 647 & 334 & 29 & 39 \\  & Time (ms) & 811 & 265 & 15 & 15 & 733 & 281 & 15 & 15 \\  & Quantity (piece) & 232 & 152 & 16 & 15 & 175 & 122 & 7 & 22 \\  & Time (ms) & 578 & 234 & 8 & 8 & 546 & 203 & 8 & 8 \\  & Quantity (piece) & 114 & 74 & 8 & 11 & 88 & 61 & 5 & 14 \\  & Time (ms) & 483 & 125 & 8 & 8 & 437 & 125 & 8 & 8 \\
2000 & Quantity (piece) & 64 & 44 & 3 & 10 & 48 & 28 & 1 & 9 \\  & Time (ms) & 468 & 125 & 8 & 8 & 406 & 125 & 8 & 8 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of image feature extraction by different threshold values of Hessian matrix determinant response value. The proposed method was applied in the mine for conveyor belt surface fault online detection, as shown in Figure 15. To further verify the effectiveness of the AMIM method, we selected two groups of images collected by two line-scan cameras with different viewpoints. The image size was 2048 \(\times\) 2048, as shown in Figure 16a,b. When \(L\) = 800, k = 353, \(T_{d}\) = 44 (\(T_{d}\) as half of the mean of image pixels), the overlapping regions of left and right images in Figure 16a were [1519,1788], [236,397], and the overlapping regions of left and right images in Figure 16(b) were [1526,1784], [257,546]. The parameters were set as T\({}_{E}\)= 20, \(L_{f}\) = 61, \(T_{C}\) = 128, \(T_{B}\) = 128. The evaluation parameters \(E\) of the IOI calculated from the image registration overlapping region in Figure 16a were 11.22 and 8.14, respectively. The evaluation parameters \(E\) were calculated by Equation (14). According to the detection algorithm of the IOI, the images in Figure 16a were identified as the non-IOI. For the overlapping region estimated from the image in Figure 16b, the evaluation parameters \(E\) of the IOI calculated were 29.06 and 36.97, respectively. The images in Figure 16b were identified as the IOI. Only for the Figure 16b, using 128 \(\times\) 128 block size, was the feature-based adaptive partition block registration method carried out. Finally, the improved weighted smoothing algorithm was used for image fusion, and the stitched image was obtained, as shown in Figure 16c,d. The subjective evaluation of the stitched image shows that the stitching effect of the AMIM method is good. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Image**} & **Number of Feature Points (Piece)** & **Registration Number (Pairs)** & **Registration Time (ms)** \\ \hline Original Image & 407 & 14 & 12 \\ ROI Image & 274 & 10 & 9 \\ Block Image & 60 & 4 & 5 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of the registration. Figure 14: Random sample consensus (RANSAC) matching of feature points. (**a**) Figure 13(a5) and Figure 13(b5) registration. (**b**) Figure 13(a6) and Figure 13(b6) registration. (**c**) Figure 13(a7) and Figure 13(b7) registration. (**d**) Figure 13(a8) and Figure 13(b8) registration. The AMIM method is evaluated from the registration rate and running time to verify the effectiveness of our method more objectively. The registration rate measures the accuracy of the matched feature points.