# DB-Net: Detecting Vehicle Smoke with Deep Block Networks
## 5 Experiments
### Ablation Study  
In this subsection, we conduct a series of experiments to demonstrate the rationality of the aggregation branch in DB-Net and verify the effectiveness of the coarse-to-fine training strategy. All the ablation studies were evaluated on the PoVSSeg-block test set.  
**Aggregation branch.** To investigate whether the aggregation branch upgrades the effeteness of DB-Net. We discard the aggregation branch to build a network with only the main branch. As can be observed in Table 2, when models were trained in the same way, the model which contains the aggregation branch consistently performed better than the model which only has the main branch. The increment in the mIoU of the smoke was even more incredible when a coarse or fine-to-coarse strategy trained both models. This demonstrates that the aggregation branch can help the model capture smoke features better, especially in coarse annotations. Furthermore, as shown in the last column of Table 2, the aggregation branch only takes an additional 1670 parameters for the model. These findings indicate that the aggregation branch significantly improved the model's performance without too much extra computational overhead.  
**Training strategy.** We compared four different training strategies, that is, Fine, Coarse, Fine-to-Coarse, and Coarse-to-Fine, to explore the impact of training strategies on the model's performance. As the results presented in Table 2 show, first of all, compared to training with the Fine strategy, both models' performance improved by training with the Coarse-to-Fine training strategy. Secondly, models obtained the worst results when only trained by a coarsely annotated dataset. Thirdly, the Fine-to-Coarse training strategy still resulted in poor performance for models. For Coarse-to-Fine and Fine-to-Coarse, in contrast, both fine and coarse datasets were utilized in the two strategies; the only difference was the training priority of the dataset. The Fine-to-Coarse strategy first trains the model by the finely annotated dataset and then tunes by the coarsely annotated dataset. Hence, the outputs of the model eventually converged to those ambiguous details in the coarsely annotated dataset, which suggests that even if the model was training with the same content, the priority still makes a massive difference in performance.  
Figure 6 illustrates the heat map of the prediction results from the DB-Net trained by different training strategies. As can be observed in Figure 6, when the finely annotated dataset was solely trained the model, it only responded strongly to the heavy smoke region. However, when the model was trained or fine-tuned using the coarsely annotated dataset, the model even recognized some background areas as the smoke region. The results are impressive when the model is trained by the coarse-to-fine training strategy. The model can be activated not only by the heavy smoke region, but also by the soft smoke region, which achieves the best prediction performance. All this proves that coarse-to-fine training is the optimal strategy for training the model to obtain better performance.