# Adaptive Multi-View Image Mosaic Method for Conveyor Belt Surface Fault Online Detection
## 5 Conclusions  
Correct operation of the conveyor belt is very important for production and staff safety. The multi-view conveyor belt surface fault online detection system based on machine vision can provide a sufficient guarantee. This paper proposed an adaptive multi-view image mosaic method based on the combination of grayscale and feature to improve the accuracy and real-time of image mosaic and realize the multi-view conveyor belt surface fault online detection. Compared with the traditional mosaic methods, the proposed method has the following improvements and advantages:  
The overlapping region of two adjacent images is preliminarily estimated by establishing the overlapping region estimation model, and then the grayscale-based method is used to register the overlapping region. For the collected image, the IOI detection algorithm is used to divide it into the IOI and the non-IOI, and different registration methods are used. For the IOI, its overlapping region may contain the fault information of the conveyor belt. The feature-based partition and block registration method is used to register the images more accurately, the overlapping region is adaptively segmented, the SURF algorithm is used to extract the feature points, and the random sample consensus RANSAC algorithm is used to achieve accurate registration. It is easy to realize the accurate measurement of the geometric shape of the fault, and it is conducive to the accurate and reliable detection and evaluation of the health status of the conveyor belt. For the non-IOI, its overlapping region almost does not contain the fault information of the conveyor belt, and the features are not obvious. It is easy to have the wrong registration by using the SIFT algorithm, SURF algorithm, and other feature-based registration methods, and the registration rate is even 0%. Thus, they cannot obtain the right result, and the process is time-consuming, which is not conducive to real-time online detection. Accurate registration is not performed on the adjacent non-IOIs, which improves the accuracy and real-time of stitching. The improved weighted smooth fusion algorithm is used to fuse the images to realize image stitching. The experimental results show that when the SURF algorithm is used to extract and describe the block image features, the threshold value of the Hessian matrix determinant response value is 1000, which is better compared with 500, 1500, and 2000. It has the advantage of extracting enough feature points and consuming less time. Enough feature points can be extracted from the block image, and the features are significant, which is easy to register accurately. Compared with the whole image registration, it takes less time. The results of the experimental analysis showed the usefulness of the proposed method. The stitching effect is better compared with the SIFT and SURF algorithms. Simultaneously, it can reduce the amount of stitching calculation and the time of the stitching process, \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multirow{2}{*}{**Image**} & **Feature Points** & **Registration** & **Mismatches** & **Registration** & \multirow{2}{*}{**Time (ms)**} \\  & & **Number** & **Number** & **Number** & **Rate** & \\ \hline \multirow{3}{*}{SIFT} & Figure 16a & 1225 & 0 & 13 & 0\% & 5513 \\  & Figure 16b & 984 & 96 & 5 & 95.04\% & 4892 \\  & Figure 16a & 232 & 0 & 11 & 0\% & 1270 \\  & Figure 16b & 175 & 94 & 5 & 94.95\% & 1231 \\ \multirow{3}{*}{AMIM} & Figure 16a & 31 & - & - & - & 278 \\  & Figure 16b & 42 & 42 & 1 & 97.67\% & 328 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison of our method with other methods. and the registration rate is high. The registration rate can reach 97.67%, and the average time of stitching two adjacent images is less than 500 ms. It is suitable for conveyor belt surface fault online detection. In addition, this method can also provide a reference for other application types of image mosaic.