## 5 Conclusions  
This paper addresses the problem of an accurate and real-time traffic sign recognition system. We propose a regressive anchor box selection algorithm that suggests the best-fit anchor set obtained majorly from the higher concentration traffic sign regions of the dataset. The obtained anchors improve precision-recall percentage and thus mean Average Precision of the network. Furthermore, we propose a modified YOLOv3 network, which is faster and more accurate than the state-of-the-art methods. The pruning of higher-level feature map benefits include reducing false positives and lowering log-average miss rate. The proposed network and approach for anchor box determination aids in obtaining a decent balance between accuracy and inference time.  
Although focal loss adjustments help in detecting smaller objects in an image, they do not work for our modified network. Our proposed network is robust enough to detect small traffic signs without focal loss adjustment implementation. It also discovered that the optimal values of alpha and gamma for the traffic sign dataset are 0.75 and 1, respectively. The proposed method is evaluated on GTSDB [3] and STS [9] datasets. Experiments show that the proposed method is more real-time, accurate, robust, and competitive than the state-of-the-art methods.  
Conceptualization, Y.R., H.A., D.M.S.B. and W.T.T.; Data curation, Y.R., H.A., D.M.S.B. and W.T.T.; Formal analysis, Y.R., H.A., D.M.S.B., W.T.T. and M.M.; Funding acquisition, M.A. and M.M.; Investigation, Y.R., H.A., D.M.S.B. and W.T.T.; Methodology, Y.R., H.A., D.M.S.B. and W.T.T.; Project administration, M.M.; Supervision, M.A.; Validation, Y.R., H.A., D.M.S.B. and M.A.; Visualization, Y.R., H.A., D.M.S.B., M.A. and M.M.; Writing--original draft, Y.R., H.A., D.M.S.B., W.T.T. and M.M.; Writing--review & editing, W.T.T., M.A. and M.M.M. All authors have read and agreed to the published version of the manuscript.  
This research has been financially supported by The Analytical Center for the Government of the Russian Federation (Agreement No. 70-2021-00143 dd. 01.11.2021, IGK 000000D730321P5Q0002). This work was also supported by the Higher Education Commission of Pakistan under the National Research Program for Universities grant number 8348.  
Not applicable.  
This research has been conducted on publicly available datasets, namely German Traffic Sign dataset and Swedish Traffic Sign dataset, which can be accessed using following links: [[http://benchmark.ini.rub.de/?section=gtsdb&subsection=dataset](http://benchmark.ini.rub.de/?section=gtsdb&subsection=dataset)]([http://benchmark.ini.rub.de/?section=gtsdb&subsection=dataset](http://benchmark.ini.rub.de/?section=gtsdb&subsection=dataset)) and [[https://www.cvl.isy.liu.se/research/datasets/traffic-signs-dataset/](https://www.cvl.isy.liu.se/research/datasets/traffic-signs-dataset/)]([https://www.cvl.isy.liu.se/research/datasets/traffic-signs-dataset/](https://www.cvl.isy.liu.se/research/datasets/traffic-signs-dataset/)), respectively.  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Gamma** & **Alpha** & **Danger** & **Mandatory** & **Prohibitory** & **mAP** & **Mean Larm** \\ \hline
0 & 0.25, 0.50, 0.75 & - & - & - & - & - \\
1.0 & 0.25 & 97.92\% & 57.92\% & 95.20\% & 83.68\% & 0.16 \\
1.0 & 0.50 & 97.61\% & 97.61\% & 93.62\% & 91.70\% & 0.04 \\
1.0 & 0.75 & 98.30\% & 85.01\% & 93.92\% & 92.41\% & 0.01 \\
1.0 & 0.80 & 91.17\% & 82.55\% & 95.77\% & 89.83\% & 0.07 \\
1.0 & 0.85 & 97.82\% & 73.21\% & 95.50\% & 88.84\% & 0.16 \\
1.0 & 0.99 & 90.00\% & 74.22\% & 94.70\% & 86.31\% & 0.09 \\
1.2 & 0.75 & 96.75\% & 70.95\% & 91.45\% & 86.38\% & 0.05 \\
1.5 & 0.75 & 93.63\% & 85.52\% & 94.96\% & 91.37\% & 0.04 \\ \hline  & Ours & 94.49\% & 90.06\% & 94.72\% & 93.09\% & 0.11 \\ \hline \end{tabular}
\end{table}
Table 3: mAP results for focal loss implementation on GTSDB dataset. Top result in each class are highlighted in bold.