# The Application of Deep Learning and Image Processing Technology in Laser Positioning
## 4 Experimental Results
The result candidates contained correct results and misrecognitions, and the result of the target images minus the result candidates showed that the program failed to recognize the number of targets. Figure 4: (**a**) Complete light spot map positioning (25 positive samples); (**b**) incomplete light spot map positioning (25 positive samples). Figure 5: (**a**) Complete light spot map positioning (50 positive samples); (**b**) incomplete light spot map positioning (50 positive samples). According to Table 1, the system still had misrecognitions. This could be because the complete light spots and incomplete lights spots were recognized using the same neural network, or that the light spots were complete but only partial regions were positioned. In addition, when the laser was shot at the platform, the brightness would decrease outwards from the laser center spot, and the laser would be partially scattered by particles in the air. Visual recognition would be difficult because of the possibility of brightness in partial black regions. If the mode of these bright regions was close to the sample of the light spot, there could be misrecognition. The region of the complete light spot was obtained successfully from the result of using 100 positive samples. According to the system flow, the CCD image center was moved to the positioning regional center and set as the template image, as shown in Figure 6.  
The aspect ratio of the region was calculated, and the aspect ratio of the template calculated by the program was 1.027. When the complete light spot was identified, the invariant moments of the template image were calculated and the first moment was taken, i.e., the barycentric coordinates of the light spot region, and these are indicated in the image, as shown in Figure 7. \begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Number of Positive Samples** & 25 & 50 & 100 & 100 (reduce feature region) \\
**Number of Negative Samples** & 50 & 100 & 200 & 200 \\
**Number of Target Images** & 100 & 100 & 100 & 100 \\
**Result Candidates** & 74 & 90 & 96 & 98 \\
**Correct Results** & 40 & 69 & 88 & 96 \\
**Accuracy (\%)** & 54.054 & 76.667 & 91.667 & 97.96 \\
**Classification Accuracy (\(\mu\))** & 0.37 & 0.284 & 0.245 & 0.14 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Experiment data of light spot positioning system. Figure 6: Positioning region template. In the final step, the distance between the image center and the light spot center was calculated, and the CCD camera was moved. The image center and light spot center were overlapped, and the light spot center was aligned for the second time, as shown in Figure 8.  
Figure 9 shows the gray level histogram of the laser spot. The positions of the light spot center and image center can be observed, and the distance between the two centers can be calculated by the coordinate relationship. Figure 8: Schematic diagram of the second alignment. Figure 7: Positioning light spot centroid. Table 2 shows the mean errors of the image center and light spot center when the system performed light spot positioning with different sample numbers, where 1 pixel = 0.11 \(\upmu\)m. It was observed that as the number of positive samples increased and the feature was enhanced, the center of the first positioning would be close to the center of the light spot and the time of the shift to the light spot center could be shortened. Figure 10 is a recognized incomplete light spot map. First, it was recognized as being range independent, and the invariant moments were calculated using the image center as a reference point, as shown in Table 3. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Number of Positive Samples** & 25 & 50 & 100 & 100 (reduce region) \\
**Average Mis-distance (pixel)** & 3.367 & 2.579 & 2.224 & 1.273 \\
**Average Mis-distance (\(\upmu\)m)** & 0.37 & 0.284 & 0.245 & 0.14 \\
**Detection Time (s)** & 0.5 \(\sim\)1.6 & 0.5 \(\sim\)1.6 & 0.5 \(\sim\)1.6 & 0.5 \(\sim\)1.6 \\ \hline \hline \end{tabular}
\end{table}