# An Improved Biomimetic Olfactory Model and Its Application in Traffic Sign Recognition
## 4 Experiment and Result Analysis
### Comparison before and after Improvement to the KIII Model  
The figure below depicts the total time spent on training and testing the KIII model for three different original image sizes (corresponding to three different channels of the KIII model) using four different numerical methods: [PERSON]'s method, the trapezoidal rule method, the third-order [PERSON] method, and the fourth-order Runge-Kutta method for \begin{table}
\begin{tabular}{c c l} \hline \hline
\begin{tabular}{l} 1 represents perfect positive correlation, \(-1\) represents perfect negative correlation, and 0 represents no correlation. \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The relationship between different measurement methods and correlation. Figure 4: Partial data display. calculating the numerical solutions of a system of differential equations. The figure also presents the average recognition accuracy on the test dataset for each numerical method. According to the results shown in Figures 5 and 6, training with different numerical methods resulted in minimal differences in test accuracy, with an average accuracy difference of less than 1%. However, there was a significant fivefold difference in time expenditure, indicating that the KIII model's pattern recognition task does not demand strict precision in numerical solution accuracy for the next time step. This also highlights the notable advantage of [PERSON]'s method in terms of time efficiency when solving numerical solutions. Additionally, it is evident that the KIII model exhibits poor recognition performance on untreated complex images, with an average accuracy of only 56.69%. Therefore, image processing and feature extraction are necessary and critical for improved performance. The following figure shows the recognition results of image data of three different sizes after applying adaptive histogram equalization, Gaussian filtering, and the fusion of global and local DCT features introduced in Section 3.2 during the feature extraction stage of the KIII model. The Euler method is used for solving the differential equations, and the experimental results are as shown in the following figure. According to the results in Figures 7 and 8, introducing a new feature extraction methods in the KIII model significantly improved the model's performance compared to previous iterations, demonstrating the rationale behind these methods. Adaptive Histogram Equalization enhances the uniformity of the grayscale distribution in original images, effectively highlighting fine details as the image's information entropy increases. Gaussian filtering reduces image noise, resulting in a smoother image. Despite the increased computational cost due to the extraction and combination of local and global image features, the average test accuracy reached 89.10%. This represents a notable 32.41%  
Figure 5: Accuracy of different solution methods. Figure 6: Time spent on different solution methods. improvement over the KIII model's direct recognition of the original images. Taking all factors into consideration, the proposed feature extraction methods have shown significant effectiveness in pattern recognition tasks for the KIII model. Finally, experiments were conducted regarding the correlation measurement methods described in Section 3.3 at the model decision stage, and the results are shown in Figure 9.  
According to the results shown in Figure 9, the performance of the Spearman correlation coefficient in the decision-making stage is significantly superior to other measurement methods. This can be attributed to several factors. Firstly, the Spearman correlation coefficient exhibits better adaptability to non-linear relationships, a crucial characteristic given  
Figure 8: Time spent after feature extraction. Figure 7: Accuracy after feature extraction. Figure 9: Accuracy of different decision-making methods. that similarity relationships among traffic signs are non-linear due to varying shooting angles, lighting conditions, and deformations.