## 4 Application Results and Discussion  
This paper uses the above-mentioned panoramic borehole image automatic recognition method to analyze the image data of eight boreholes in the Jinpingzi landslide area. For the panoramic borehole image data obtained by the DPBCS for each hole, the system automatically preprocesses the image to obtain some initial data. Next, the full-hole image is automatically divided and the structure plane areas are selected. After that, the structure planes in each area are automatically identified. Finally, the automatic recognition results and related structure surface parameters are saved to the relevant file for reference. This method enables the automatic identification of borehole images and statistical analysis  
Figure 6: Sine function matching results of regions. (**a**) Results for Figure 3a; (**b**) Results for Figure 3b. of parameters. The results from applying this method for the automatic identification of structural plane parameters to the images presented in Figure 6 are shown in Table 1.  
Table 1 presents the results generated by the automatic recognition technology for panoramic borehole images when applied to the images from Figure 6. In order to determine the effects of the automatic recognition method, we used the structural surface area divisions shown in Figure 5. This produced the sine curve that is most suitable to describe the structural surface characteristics of the rock mass within the determination line (red lines) for each area, as shown in Figure 6. Furthermore, it can be seen from Figure 6 that the automatically identified sine curves are in good agreement with the rock structure planes, which is in line with engineering practice. To improve the recognition rate and accuracy of the structural plane recognition method described in this article, we performed a deeper fine matching of structural surface sine functions based on the two steps mentioned in Section 2. The specific operation method is: based on the result of the last sinusoidal curve matching, in each divided area, taking the y0 value obtained in the last matching as the baseline value, increase and decrease it by 20%, and then increase the y value by line moving unit sequentially. Similarly, \(\beta\) and \(\alpha\) are also determined by taking the last matching value as a baseline, increasing, and decreasing by \(3^{\circ}\), and then sequentially increasing them in steps of \(0.1^{\circ}\). This enables finer recognition of panoramic borehole images, which in turn makes it possible to obtain more accurate structural plane parameters, reduces the error rate, and also makes up for the omission of some structural planes. Figure 7 and Table 2 present the results obtained after refined identification was applied to the image shown in Figure 6b. It can be seen from Figure 7 that the missing structural planes (those numbered 2 and 4 in Figure 7) can be re-identified. It can be seen from Table 2 that the structural surface parameters obtained after the sine curve matching are much accurate. In the automatic identification of panoramic borehole images, the software system of the digital panoramic camera system can automatically identify all structural planes in the entire borehole image at the same time. Moreover, it can obtain the position, dip direction, dip, gap width and other vital parameters of each structural plane. In order to compare and analyze the practical effectiveness of the automatic recognition of full-hole images, the authors conducted a statistical analysis of the automatic recognition results and compared them with the manual interpretation results. The outcomes of the statistical analysis are shown in Table 3.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**No.