# An Automatic Counting Algorithm for Topographic Maps Based on Videos for Map Management
## 4 Results
### Analysis of Paper Map Inventory Results Based on Fusion Window Counting Model  
Combining the target detection results with the image quality assessment index, the counting experiments for multiple videos were carried out according to three methods: the adaptive window, the fixed window, and the fusion window. Because the FFP-DPM-ROI algorithm improved the operation speed with less loss of detection accuracy in the experimental results for object detection, we combined it with the SMD clarity assessment function (hereinafter called the FFP-DPM-ROI-SMD algorithm) for the inventory research of topographic maps. The fusion window method in this algorithm is the fusion window counting model proposed in this paper. Taking video 3 as an example, the counting process of the FFP-DPM-ROI-SMD algorithm is shown in Figure 12. The counting results of this  
Figure 11: Abnormal detection results and smoothing. Figure 10: The average time and average accuracy of the three algorithms. algorithm and the DPM algorithm combined with the SMD clarity assessment function (hereinafter referred to as the DPM-SMD algorithm) were compared and analyzed, as shown in Figure 13, where the left figure shows the counting results of the DPM-SMD algorithm and the right figure shows the counting results of the FFP-DPM-ROI-SMD algorithm. Table 3 shows the counting accuracies of the different algorithms. As can be seen in Figure 12, for the first interval without mechanical wheels, there is one extremum in the adaptive window method and there are two extremums in the fixed window method, and according to the idea of a fusion window, the fusion window counting model does not add one to the count value for this interval. The observation of the videos shows that the topographic maps are not updated in the first cycle when the mechanical wheel appears; so, the fusion window method is more in line with the counting of the topographic maps in the actual videos. The comparison and analysis of the two algorithms in Figure 13 and Table 3 reveal that both of the adaptive window methods have the same counting results and higher accuracy, and the count results of the two fixed window methods are greatly affected by the accuracy of the object detection algorithm and have lower count accuracy. The counting results of the fusion window method for all the videos except video 2 are closest to the actual number of maps, and the average counting accuracy of the FFP-DPM-ROI-SMD algorithm is slightly lower than that of the DPM-SMD algorithm. In short, we have shown that the FFP-DPM-ROI algorithm  
Figure 12: FFP–DPM–ROI–SMD algorithm inventory process. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Video**} & \multicolumn{3}{c}{**DPM–SMD**} & \multicolumn{3}{c}{**FFP–DPM–ROI–SMD**} \\ \cline{2-7}  & **Adaptive** & **Fixed** & **Fusion** & **Adaptive** & **Fixed** & **Fusion** \\  & **Window** & **Window** & **Window** & **Window** & **Window** & **Window** \\ \hline
1 & 95\% & 83\% & 99\% & 95\% & 85\% & 98\% \\
2 & 99\% & 95\% & 92\% & 99\% & 95\% & 93\% \\
3 & 98\% & 96\% & 99\% & 98\% & 97\% & 99\% \\
4 & 91\% & 88\% & 95\% & 91\% & 82\% & 92\% \\
5 & 91\% & 88\% & 95\% & 91\% & 82\% & 92\% \\ Average accuracy & & 96\% & & & 95\% \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of counting accuracies of different algorithms. affects the final counting accuracy while increasing the detection speed, leading to a slight decrease in detection accuracy. It also affects the counting results of the final fusion window method, mainly by changing the counting process of the fixed window method. When we combined these results with the actual data, we found that the counting accuracies of the two algorithms for video 2 were low. This was due to the shooting angle, which caused the sheet designation area to be blocked in the later stages of the video.