## 4 Conclusions  
We proposed a lightweight and high-accuracy bus detection model based on a YOLOv5 architecture in this study to address the significant challenges that individuals with visual impairment face while waiting for buses. The Improved-YOLOv5, our proposed model, integrates the GhostConv and C3 Ghost Modules into the YOLOv5 network, implements a slim scale detection model, and replaces the SPPF in the backbone with SimSPPF for increased computational efficiency and accurate object detection capabilities. By reducing the number of parameters and FLOPs, our proposed model outperformed the original YOLOv5 model in terms of the precision, recall, and [EMAIL_ADDRESS]. The Improved-YOLOv5 model outperformed other object detection models in terms of the accuracy and inference time, as demonstrated by our experimental results. Furthermore, because of its smaller size, lower memory usage, and faster inference time capabilities, the proposed model was more efficient. This makes it a more viable option for mobile devices and real-time applications, especially in resource-constrained scenarios.  
The proposed bus detection model has the potential to assist individuals with visual impairment in identifying buses, thereby increasing their independence and quality of life. It can also be integrated into bus detection systems, improving public transportation services, particularly in urban areas. Our proposed model has significant potential benefits for assisting individuals with visual impairment and improving public transportation services, and we believe that our research will inspire further development and research in this area. However, several challenges remain to be addressed, such as improving the model's generalizability across different datasets and environments. More research is needed to investigate the effectiveness of our proposed model under various lighting  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Inference/ms** & **[EMAIL_ADDRESS]** & **Parameters** & **GFLOPs** \\ \hline YOLOv3 & 2.3 & 0.925 & 61,508,200 & 154.6 \\ YOLOv3-tiny & 2.8 & 0.888 & 8,671,312 & 12.9 \\ YOLOv5s & 5 & 0.924 & 7,018,216 & 15.8 \\ YOLOv5m & 7.9 & 0.923 & 20,861,016 & 47.9 \\ YOLOv5l & 15 & 0.896 & 46,119,048 & 107.7 \\ YOLOv5x & 15 & 0.924 & 86,186,872 & 203 \\ YOLOv8n & 3.1 & 0.932 & 3,006,233 & 8.1 \\ Improved-YOLOv5 & 2.1 & 0.933 & 717,020 & 2.1 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Performance comparison of different models.  
Figure 11: Performance comparison of different models in graphical form.  
conditions and environmental factors. Nonetheless, our findings show that object detection models based on the Improved-YOLOv5 have the potential to address real-world problems and contribute to the development of more efficient and accurate bus detection systems, paving the way for future research and development in this field.  
Conceptualization, R.A. and C.W.; methodology, R.A. and C.W.; software, R.A.; validation, R.A. and C.W.; formal analysis, R.A.; investigation, R.A., S.E. and C.W.; resources, R.A. and S.E.; data curation, R.A.; writing--original draft preparation, R.A.; writing--review and editing, R.A. and C.W.; visualization, R.A.; supervision, C.W. All authors have read and agreed to the published version of the manuscript.  
R.A. is supported by a scholarship from the Ministry of Education, Culture, Sports, Science and Technology of Japan (MEXT) scholarship.  
Not applicable.  
Data Availability Statement: Data are stored in a password-protected PC in the Kyushu Institute of Technology.  
The authors declare no conflict of interest.