# An Object Detection Method Based on Feature Uncertainty Domain Adaptation for Autonomous Driving
## 4 Experimental Analysis
### Experimental Results of Cityscapes to Cityscapes Foggy  
The impact of fog on images is characterized by significant local noise, which can randomly cause occlusion and blurring of the targets in some areas. Table 1 presents a comparison of detection results in Cityscapes foggy. These models only obtained ground truth labels from the source domain. When the basic FRCNN model was not adaptively trained for the target domain, only 20.0% of mAP is obtained in foggy scenes. In contrast, \"Target\" achieved a result of 42.3% by directly training on labels from foggy data. This result is only provided as a reference and is not included in the comparison because obtaining labels from the target domain is not feasible in practice. After adopting different domain adaptation methods, an obvious improvement in detection performance relative to FRCNN was observed. The proposed method achieved the highest detection results in multiple categories and had the highest mAP among all methods presented in Table 1.  
Compared with FRCNN, FUDA has the greatest improvement effect in the classification of Person, Rider, and Car and Bicycle, and the improvement in the mAP results reached 101%. It is worth noting that after training with the domain adaptation method, the results of several classifications exceed the results of training directly with the target domain labels. The reason is that under the constraint of adversarial training, the optimization objective of the feature extraction layer is opposite to that of the domain discriminator. In order to deceive the domain discriminator, the feature extraction layer will gradually make the output feature map uniform with the number of training iterations while meeting the target detection requirements. In other words, ideally when the feature distributions from the source and target domains are consistent, the domain discriminator will not be able to accurately judge the current domain classification. This indicates that the extracted features are domain-invariant, and the model obtained more generalized and universal target features.  
The precision-recall curve of the proposed model in domain adaptation from Cityscapes to Cityscapes foggy is presented in Figure 6. Recall is the ratio of the number of correctly detected objects to all ground truth instances. Precision in Figure 6 indicates the percentage of correctly detected objects at different recall rates. It can be observed that the proposed model exhibits a clear advantage in the [0.2-0.6] interval.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline
**Methods** & **Person** & **Rider** & **Car** & **Truck** & **Bus** & **Train** & **Motor** & **Bicycle** & **mAP** \\ \hline FRCNN [1] & 24.2 & 28.5 & 34.6 & 10.1 & 25.6 & 4.0 & 11.7 & 20.9 & 20.0 \\ DA [25] & 25.0 & 31.0 & 40.5 & 22.1 & 35.3 & 20.2 & 27.1 & 20.0 & 27.6 \\ SWDA [26] & 29.9 & 42.3 & 43.5 & 24.5 & 36.2 & 32.6 & 30.0 & 35.3 & 34.3 \\ MAF [28] & 28.2 & 39.5 & 43.9 & 23.8 & 39.9 & 33.3 & 29.2 & 33.9 & 34.0 \\ DAM [39] & 30.8 & 40.5 & 44.3 & 27.2 & 38.4 & 34.5 & 28.4 & 32.2 & 34.6 \\ CST [40] & 32.7 & 44.4 & 50.1 & 21.7 & **45.6** & 25.4 & 30.1 & 36.8 & 35.9 \\ CDN [41] & 35.8 & 45.7 & 50.9 & 30.1 & 42.5 & 29.8 & **30.8** & 36.5 & 36.6 \\ CFFA [42] & 43.2 & 37.4 & 52.1 & **34.7** & 34.0 & **46.9** & 29.9 & 30.8 & 38.6 \\ RPNPA [43] & 33.3 & 45.6 & 50.5 & 30.4 & 43.6 & 42.0 & 29.7 & 36.8 & 39.0 \\ FUDA (Ours) & **43.9** & **49.1** & **53.9** & 28.4 & 41.8 & 31.6 & 28.2 & **45.0** & **40.2** \\ \hline Target & 36.2 & 46.5 & 55.8 & 34.0 & 53.1 & 40.2 & 36.0 & 36.4 & 42.3 \\ \hline \end{tabular}
\end{table}
Table 1: Experimental results (%) of Cityscapes to Cityscapes Foggy. Bold indicates the highest result.