# DB-Net: Detecting Vehicle Smoke with Deep Block Networks
## 6 Conclusions and Future Work  
In this paper, we elaborately designed the DB-Net, a conceptually new yet simple model for block-wise vehicle smoke detection, and employed a coarse-to-fine training strategy to train the DB-Net. Moreover, we built a real-world vehicle smoke dataset with polygonal annotation, the PoVSSeg, since there is no available dataset, which has further converted to a block-wise annotated dataset to fit the structure of DB-Net. Our method has been demonstrated as effective through a series of experiments, which achieved 71.14% global mIoU in the PoVSSeg-block test set with only 8.54 million parameters and can run at 59.12 FPS on \(960\times 540\) high-resolution images. In conclusion, our method achieved the best trade-off in computational efficiency and detection accuracy than other methods. Future research could explore the vehicle smoke detection issues on multi-feature fusion, such as spatial features with temporal features, to improve detection accuracy.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{3}{c}{**Scene 1**} & \multicolumn{3}{c}{**Scene 2**} & \multicolumn{3}{c}{**Scene 3**} & \multicolumn{3}{c}{**Scene 4**} \\ \cline{2-13}  & **Recall** & **Precision** & **F1** & **Recall** & **Precision** & **F1** & **Recall** & **Precision** & **F1** & **Recall** & **Precision** & **F1** \\ \hline R-VLBC & 0.8155 & 0.4045 & 0.5407 & 0.8021 & 0.3942 & 0.5285 & 0.9259 & 0.1663 & 0.2819 & 0.8381 & 0.3038 & 0.4459 \\ \hline PSP-Net & 0.4234 & 0.4594 & 0.4407 & 0.2212 & 0.4949 & 0.3058 & 0.7451 & 0.1436 & 0.2408 & 0.7743 & 0.4133 & 0.5389 \\ DeepLab-v3 & 0.8235 & 0.2384 & 0.3698 & 0.826 & 0.2518 & 0.3859 & 0.9976 & 0.0631 & 0.1186 & 0.9805 & 0.1379 & 0.2418 \\ FCN-32s & 0.4791 & 0.4961 & 0.4874 & 0.3491 & 0.625 & 0.4479 & 0.8413 & 0.1525 & 0.2582 & 0.7061 & 0.5129 & 0.5942 \\ PAN & 0.6638 & 0.4617 & 0.5446 & 0.7016 & 0.3999 & 0.5094 & 0.9013 & 0.1254 & 0.2202 & 0.8685 & 0.2649 & 0.406 \\ \hline DB-Net(Ours) & 0.6161 & 0.6283 & 0.6221 & 0.6341 & 0.5864 & 0.6093 & 0.9242 & 0.1155 & 0.2053 & 0.8246 & 0.3895 & 0.5291 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison between our method and state-of-the-art methods with varied metrics in LoSSoV-video test set.  
Figure 8: Average Recall, F1, and Precision in four senses.  
**Author Contributions:** Conceptualization, J.C. and X.P.; methodology, J.C. and X.P.; software, J.C.; data curation, J.C. and X.P.; writing--original draft preparation, J.C.; writing--review and editing, J.C. and X.P.; All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work is partially supported by the National Natural Science Foundation of China (62176165), the Stable Support Projects for Shenzhen Higher Education Institutions (SZWD2020101), and the Natural Science Foundation of Top Talent of SZTU(grant no.GDRC202131).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Data available on request due to restrictions.  
**Conflicts of Interest:** The authors declare no conflict of interest.