# Privacy-Preserved Visual Simultaneous Localization and Mapping Based on a Dual-Component Approach
## 4 Results
### Performance of Object Detection  
The performance of the privacy region detection algorithm was evaluated on KITTI's Object Detection Evaluation dataset. It contains 7481 training images and 7518 test images, with 80,256 labeled cars and pedestrians. Evaluation metrics included Precision (P), Recall (R), Mean Average Precision at a 50% Intersection over Union (IoU) threshold (mAP@50), and Mean Average Precision averaged over IoU thresholds from 50% to 95% (mAP@50-95),along with the number of model parameters. The formula for mAP@50, computed across n categories, is given as:  
\[\text{mAP@50}=\frac{1}{n}\sum_{i=1}^{n}\int_{0}^{1}\text{P}(\text{R})dR \tag{2}\]  
where P and R denote Precision and Recall, respectively, defined as:  
\[\text{P}=\frac{\text{TP}}{\text{TP}+\text{FP}} \tag{3}\]  
\[\text{R}=\frac{\text{TP}}{\text{TP}+\text{FN}} \tag{4}\]  
where TP, FP, and FN represent the number of true positives, false positives, and false negatives, respectively.  
RT-DETR [28], YOLOv3 [29], YOLOv5, YOLOv6, and YOLOv11 were used as comparison models. All experimental results of these models are obtained in the same experimental environment as the proposed method.  
The experimental results on the KITTI object detection dataset are summarized in Table 1. The applied model outperforms existing models across several key performance metrics. Despite having only 2.7 M parameters, significantly fewer than most other models, it achieves strong Precision, Recall, and mAP. Notably, it attains 92.4% Precision, second only to YOLOv3 (92.9%). Given that it uses just 2.6% of YOLOv3's parameters, this result highlights its efficiency. Moreover, it surpasses RT-DETR, which achieves only 41.0% Precision while using more parameters, further demonstrating its effectiveness.  
In terms of Recall, the applied model achieves the highest value of 87.8%, surpassing YOLOv5 (86.2%), YOLOv6 (83.5%), and YOLOv11 (85.6%), demonstrating its ability to correctly detect more positive samples. Regarding mAP@50 and mAP@50-95, it attains 93.7% and 70.7%, respectively, outperforming all other models. These results highlight its superior detection performance and overall effectiveness while maintaining a compact model size.  
Figure 4 presents a visual comparison of the detection results from the applied model and the YOLOv11 model on the test set of the KITTI Object Detection Evaluation. The upper subfigure shows a scenario with numerous pedestrians, while the lower subfigure focuses on vehicle detection. In pedestrian detection, the applied model identifies more correct instances than YOLOv11, ensuring better coverage of sensitive areas and enhancing privacy protection. This also demonstrates its superior ability to detect objects in crowded environments, which is crucial for privacy-preserving applications. For vehicle detection, the applied model accurately identifies vehicles without false positives, unlike YOLOv11. This reduces the need for subsequent image inpainting processing, minimizing computational overhead. Moreover, accurate detection reduces interference with visual  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **Parameters (M)** & **Precision \(P\)** & **Recall \(R\)** & **mAP@50** & **mAP@50-95** \\ \hline
**Applied Model** & **2.7** & **92.4** & **87.8** & **93.7** & **70.7** \\ RT-DETR & 32 & 41.0 & 33.0 & 36.3 & 12.0 \\ YOLOv3 & 103 & 92.9 & 68.0 & 77.5 & 69.7 \\ YOLOv5 & 2.2 & 91.4 & 86.2 & 93.2 & 68.9 \\ YOLOv6 & 4.2 & 91.3 & 83.5 & 91.2 & 66.8 \\ YOLOv11 & 2.6 & 91.1 & 85.6 & 92.8 & 68.7 \\ \hline \hline \end{tabular}  
We applied YOLOv8 as privacy sensitive object detector.  
\end{table}
Table 1: Comparison of applied model with other models on the KITTI Object Detection Evaluation 2012 dataset.