# DB-Net: Detecting Vehicle Smoke with Deep Block Networks
## 5 Experiments
### Performance Comparisons  
In this subsection, we compare our method with several popular deep learning semantic segmentation methods (e.g., FCN-32s [20], PSP-Net [28], DeepLab-v3 [29], PAN [30]), and traditional image process based methods (e.g., R-VLBC [14]). We first report the comparison results about block-level prediction accuracy on the PoVSSeg test set and analyze the speed. Then, we show the results on the LaSSoV-video test set to contrast our method with other methods on frame-level prediction accuracy. Note that all the deep learning segmentation methods use a 50-layer ResNet as the backbone in the evaluation experiments.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Structure** & & & \multicolumn{4}{c}{**Training Strategy**} & \multicolumn{4}{c}{**Evaluation**} \\ \hline
**Aggregation** & **Fine** & **Coarse** & **Fine-to-Coarse** & **Coarse-to-Fine** & \multicolumn{4}{c}{**mIoU(\%)**} & **Parameters** \\ \cline{5-8}
**Branch** & **Fine** & **Coarse** & **Fine-to-Coarse** & **Coarse-to-Fine** & **Background** & **Smoke** & **Global** & \\ \hline  & ✓ & & & & 99.21 & 36.25 & 67.73 & 8,545,346 \\  & & ✓ & & & & 98.84 & 17.58 & 58.21 & - \\  & & & ✓ & & & 98.84 & 17.93 & 58.38 & - \\  & & & & ✓ & & 99.16 & 40.31 & 69.73 & - \\  & ✓ & & & & & 99.18 & 36.99 & 68.08 & 8,547,016 \\  & & & ✓ & & & & 98.87 & 24.28 & 61.57 & - \\  & & & & ✓ & & & 98.91 & 24.95 & 61.93 & - \\  & & & & & ✓ & & 99.17 & 43.12 & 71.14 & - \\ \hline \hline \end{tabular}
\end{table}
Table 2: Ablation study results on the PoVSSeg test set.  
**Block prediction accuracy and parameter comparisons.** The deep learning segmentation method outputted pixel-level prediction results. For evaluating various methods on the same criterion, the pixel-level results from the deep learning segmentation method are transferred to block-level results. Here we show the results in the PoVSSeg test set. Figure 7 illustrates the visualization results of each method. The first column demonstrates the ground truth, and the remaining shows different methods' results. Table 3 presents the comparison results. As shown in Table 3, our DB-Net achieves 71.14% global mIoU with only 8.54 million parameters, which is an impressive result. Furthermore, as can be observed, compared with other methods, we only use half or even less of their parameter to keep a comparable performance. Additionally, we also list the FPS in Table 3 for reference. DB-net can process a \(960\times 540\) image at the speed of 59.12 FPS. The DeepLab-v3 is the most accurate method and has a 3.3% accuracy level higher than ours.  
**Frame level prediction accuracy comparisons.** We applied several popular frame-level metrics for evaluation. For a fair comparison of the performance among the methods, the detecting thresholds were set to 0.2 for all methods. The evaluation results in each scene of the LoSSoV-video test set are presented in Table 4, and the average Recall, Precision, and F1 in four scenes are further presented in Figure 8. As can be seen in Table 4, our DB-Net achieved the best performance in Scenes 1 and 2 and also attained remarkable performance in Scenes 3 and 4, which proves that our method has better robustness than other methods for different environments. Moreover, as shown in Figure 8, the Deeplab-v3 and FCN-32s achieved the best recall and precision, respectively. Both of them still underperformed in F1. Our method performed the best F1, indicating that we achieved the best trade-off between recall and precision. Except for our method, the R-VLBC, a hand-crafted method designed explicitly for smoke detection, outperformed the deep learning segmentation methods due to the inherent difference between the smoke detection task and the semantic segmentation task.