# A Multi-Loop Vehicle-Counting Method under Gray Mode and RGB Mode
## 5 Discussion
### Discussion of Influencing Factors and Countermeasures  
Let us give a discussion of the influencing factors from the aspects of the characteristics mentioned in Table 8: road category, number of lanes, weather and shooting angle, and the countermeasures of the proposed method. * _Road Category_ Road category is a relatively important factor that has a certain influence on both vehicle detection and vehicle counting. Specifically, its influence on the results is mainly reflected in the complexity of the traffic environment and road environment. Compared with videos taken from the highways (Figures 15-17), the traffic environment and road environment of videos taken from urban roads (Figure 18) and expressways (Figures 19 and 20) are more complex, such as the higher proportion of large vehicles and more trees on both sides of the road. In the same way, videos taken from urban roads may contain more disadvantages than videos taken from expressways. Therefore, it is not enough to carry out the experiment only on the highway. We also added three groups of experiments on urban roads and expressways. The influence of the road category could also be reflected in the experiment. As shown in Table 15, the three benchmarks (Video I, Video II, and Video III), which were all taken from highways, achieved the highest accuracy. Although the accuracy of urban roads (Video IV) and expressways (Video V and Video VI) is not as good as that of the highway, it is not bad either. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & _True_ & _Counted_ & _Recall_ & _Precision_ & _F-Measure_ & _Accuracy_ \\ \hline [PERSON] [38] & 27 & N/A & 85.40\% & 95.10\% & 89.99\% & N/A \\ [PERSON] [39] & 16 & 14 & 88.89\% & 100\% & 94.12\% & 87.50\% \\ [PERSON] [37] & 27 & 29 & 100\% & 93.10\% & 96.43\% & 92.59\% \\ [PERSON] [40] & 27 & N/A & 99.20\% & 98.20\% & 98.70\% & N/A \\ [PERSON] [41] & 27 & 26 & 96.43\% & 100\% & 98.18\% & 96.30\% \\ \hline
**Proposed Method** & **27** & **27** & **100\%** & **100\%** & **100\%** & **100\%** \\ \hline \hline \end{tabular}
\end{table} 
Table 16: Comparison results of the benchmark Highway. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & _True_ & _Counted_ & _Recall_ & _Precision_ & _F-Measure_ & _Accuracy_ \\ \hline [PERSON] [42] & 77 & 69 & 89.61\% & 100\% & 94.52\% & 89.61\% \\ [PERSON] [34] & 77 & 75 & 97.40\% & 100\% & 98.68\% & 97.40\% \\ [PERSON] [39] & 77 & 71 & 88.51\% & 95.06\% & 91.67\% & 92.21\% \\ [PERSON] [37] & 77 & 76 & 98.72\% & 100\% & 99.35\% & 98.70\% \\ [PERSON] [43] & 77 & 76 & N/A & N/A & N/A & 98.70\% \\ \hline
**Proposed Method** & **254** & **256** & **100\%** & **99.22\%** & **99.61\%** & **99.21\%** \\ \hline \hline \end{tabular}
\end{table} 
Table 17: Comparison results of the benchmark M-30. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & _True_ & _Counted_ & _Recall_ & _Precision_ & _F-Measure_ & _Accuracy_ \\ \hline [PERSON] [42] & 42 & 33 & 78.57\% & 100\% & 88.00\% & 78.57\% \\ [PERSON] [34] & 42 & 39 & 92.86\% & 100\% & 96.30\% & 92.86\% \\ [PERSON] [39] & 42 & 37 & 89.36\% & 100\% & 94.38\% & 88.10\% \\ [PERSON] [37] & 42 & 42 & 100\% & 100\% & 100\% & 100\% \\ [PERSON] [43] & 42 & 40 & N/A & N/A & N/A & 95.24\% \\ \hline
**Proposed Method** & **234** & **235** & **99.57\%** & **99.15\%** & **99.36\%** & **99.57\%** \\ \hline \hline \end{tabular}
\end{table} 
Table 18: Comparison results of the benchmark M-30-HD. * _Number of Lanes_  
The influence of lane number is mainly reflected in the vehicle count. To be specific, it basically shows the trend that the less lanes, the higher the accuracy of the results. This is mainly because vehicle counting is easily affected by vehicles driving side by side in different lanes. At the same time, as the number of lanes increases, drivers have more choices of roads to take, which makes it easier for drivers to engage in driving behaviors that do not strictly follow the designated lane lines.