## 4 Evaluation and Discussion
Neutrosophic logic, with its three levels of membership, is able to effectively capture ambiguity and provide solutions that are close to reality. By including a neutrosophic set in the model, we may reduce the number of times visually impaired people collide with obstacles. In the second set of experiments, we compared the effectiveness of our proposed model to that of one that uses visual sensors to determine the shape of obstacles as in the work presented in [39]. The results are shown in Table 6. The efficiency of the proposed model in increasing accuracy by about 3% can be attributed, to the fact that the neutrosophic module in our model uses wavelet-based ultrasonic distance characteristics rather than user position based on two visual sensors, as is the case with the conventional camera-based obstacle detection paradigm. In general, there are still issues with current methods of object detection using cameras. These issues include viewpoint variation, deformation, occlusion, illumination conditions, a cluttered or textured background, and finally intra-class variation. Make sure there is enough variety in the training data to develop a strong object detector capable of resolving these typical object detection issues. Take use of different viewpoints, illumination conditions, and objects in different backgrounds. Data augmentation methods (e.g., ultrasonic sensors to extract distance measurements) may be used to synthesize the necessary data if you cannot locate real-world training data with all these variables. According to the average rate of obstacle detection for users, as seen in Table 7, left-side obstacle detection was 89%, right-side obstacle detection was 86%, and front obstacle detection was 97%. Many-massage feedback caused the user to get confused when there were multiple obstacles in the user's immediate vicinity. \begin{table}
\begin{tabular}{c c} \hline \hline
**Model** & **Accuracy for Avoiding Obstacles (Average)** \\ \hline Ultrasonic sensor-based model & 97\% \\ \hline Visual sensor–based model [39] & 94\% \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparative results for different type of sensors for obstacle detection. \begin{table}
\begin{tabular}{c c} \hline \hline
**Model Version** & **Accuracy for Avoiding Obstacles** \\ \hline Neurotropic-based version & 97.71\% \\ \hline Fuzzy-based version & 91.27\% \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Evaluation results for the tested dataset. To calculate the time complexity of the whole system, we had to combine the time complexity of the detection algorithm with the time complexity of the obstacle avoidance algorithm. Our method for obstacle avoidance is implemented after a sensor-based feature extraction technique for object recognition has been implemented, which takes less memory and computation time than prior systems [39]. The suggested obstacle avoidance method has a linear time complexity of \(O\left(n\right)\), where n is the number of identified obstacles. This indicates that our system as a whole delivers a quicker and more reliable obstacle avoidance mechanism. For more experiments close to this field of research, the reader can refer to [52; 53; 54; 55; 56; 57; 58; 59; 60].