# A Parallel Open-World Object Detection Framework with Uncertainty Mitigation for Campus Monitoring
## 4 Results
### State-of-the-Art Comparison  
Table 1 shows the comparison of our method respectively with the state-of-the-art methods according to traditional detection metrics, such as mAP and Recall. Note that after completing incremental learning of the task 4, all our classes have become known, and there are no longer any unknown targets in the test set. As a result, metrics related to unknown targets, such as U-mAP, UDR, UDP WI, etc., are not included. From Table 1, our method outperforms other state-of-the-art OWOD methods in most cases. Specifically, the unknown mAP of our method (4.84) is almost 7 times that for the model ORE (0.71)  
Figure 3: The loss curve during the training. (**a**) The loss curve of the known object detector and the objectness-driven discovery module during the initial training stage of the task 1. (**b**) The loss curve of the known object detector during the incremental learning stage of the task 1, task 2 and task 4.  
with the better known mAP performance, while the unknown class recall of our method reaches 17.40 and is higher than OW-DETR by 9.75, which is the best method in the existing works. These phenomena clearly demonstrate the effectiveness of our parallel decoupling OWOD framework. As novel known classes are added in the subsequent tasks, the recognition ability of unknown classes decreases, which is consistent with the performance of ORE. However, our method still outperforms the comparison methods by a large margin in U-mAP and unknown Recall metrics. And our known mAP performance on the newly-annotated and previous known classes is maintained. That is to say, our model is capable of promoting the collaborative learning of both known and unknown classes with fewer uncertainties. Table 2 further shows the comparison of our method respectively with the state-of-the-art methods according to common-used open-set evaluation metrics, such as WI, UDR, and UDP. From Table 2, it is evident that our method achieves similar WI scores to state-of-the-art methods while obtaining higher UDR and UDP scores. Specifically, our framework approaches or outperforms existing methods comprehensively in UDR at every stage, indicating that our framework helps discover more unknown objects. Moreover, our framework exhibits a significant improvement in UDP, suggesting that our framework has a more accurate classification capability for the discovered unknown objects. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Task IDs (\(\rightarrow\))** & \multicolumn{3}{c}{**Task 1**} & \multicolumn{3}{c}{**Task 2**} & \multicolumn{3}{c}{**Task 3**} \\ \hline  & \begin{tabular}{c} K-mAP \\ (\(\uparrow\)) \\ \end{tabular} & \begin{tabular}{c} U-mAP \\ (\(\uparrow\)) \\ \end{tabular} & \begin{tabular}{c} U-Recall \\ (\(\uparrow\)) \\ \end{tabular} & \begin{tabular}{c} K-mAP \\ (\(\uparrow\)) \\ \end{tabular} & \begin{tabular}{c} U-mAP \\ (\(\uparrow\)) \\ \end{tabular} & \begin{tabular}{c} U-Recall \\ (\(\uparrow\)) \\ \end{tabular} & \begin{tabular}{c} K-mAP \\ (\(\uparrow\)) \\ \end{tabular} & \begin{tabular}{c} U-mAP \\ (\(\uparrow\)) \\ \end{tabular} & \
\begin{tabular}{c} U-Recall \\ (\(\uparrow\)) \\ \end{tabular} &
\begin{tabular}{c} K-mAP \\ (\(\uparrow\)) \\ \end{tabular} \\ \hline Faster RCNN [57] & 56.94 & 0 & 0 & 41.56 & 0 & 0 & 32.41 & 0 & 0 & 27.03 \\ ORE [14] & 56.49 & 0.71 & 5.72 & 39.64 & 0.14 & 2.66 & 20.17 & 0.12 & 3.34 & 25.95 \\ SA [52] & 55.56 & 0.20 & 1.93 & 39.02 & 0.03 & 0.79 & 31.54 & 0.003 & 0.12 & 26.42 \\ \hline D-DETR [16] & 59.75 & 0 & 0 & 46.08 & 0 & 0 & 38.28 & 0 & 0 & 30.60 \\ OW-DETR [17] & 58.78 & 0.07 & 7.65 & 44.11 & 0.04 & 5.83 & 35.96 & 0.03 & 5.97 & 27.94 \\ \hline Ours & **59.84** & **4.84** & **17.40** & **46.38** & **1.60** & **13.53** & **38.37** & **1.45** & **14.30** & **32.63** \\ \hline \hline \end{tabular}
\end{table} 
Table 1: State-of-the-art comparison for OWOD according to traditional detection metrics. “K-” indicates the known classes, and “U-” represents the unknown classes.