# Residual Transformer YOLO for Detecting Multi-Scale Crowded Pedestrian
## 4 Experiments and Results
### Validity Verification  
To verify the effectiveness and robustness of the RT-YOLO algorithm through generalization experiments, experimental data are selected for Pascal VOC2007, which contains 20 categories and can be used to assess the performance of each algorithm. By comparing the RT-YOLO algorithm with other algorithms, the experimental results are presented in Figure 16 and Table 7. According to experimental results, RT-YOLO exhibits excellent performance on Pascal VOC2007, surpassing classical object detection algorithms in all categories. The RT-YOLO achieves a 5.4% improvement in \(mAP_{50}\) compared to YOLOv7.  
In RT-YOLO, BOTrans performs multi-head self-attention computations, handling different features and channels in parallel. This enables the identification of features for small-scale and occluded objects, as well as the extraction of features for large-scale objects. RT-YOLO integrates scale-diverse features across different network layers. It directs the  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Methods** & \(mAP_{50}\)**(\%)** & **Aero** & **Bicycle** & **Bird** & **Boat** & **Bottle** & **Bus** & **Car** & **Cat** & **Chair** & **Cow** \\ \hline Faster-RCNN [9] & 73.3 & 76.5 & 79.1 & 70.1 & 65.5 & 52.2 & 83.2 & 84.7 & 86.5 & 52.1 & 81.9 \\ SSD [30] & 74.3 & 75.5 & 80.2 & 72.3 & 66.3 & 47.6 & 83.1 & 84.2 & 86.1 & 54.7 & 78.3 \\ YOLOv5-l & 82.2 & 91.9 & 95.5 & 80.2 & 84.2 & 79.5 & 99.7 & 91.6 & 95.8 & 65.3 & 85.4 \\ YOLOX-l [18] & 84.4 & 90.3 & 94.1 & 86.1 & 81.4 & 76.4 & 99.5 & 94.4 & 95.1 & 74.4 & 92.5 \\ YOLOV7 [14] & 89.2 & 93.8 & 98.5 & 82.5 & 90.6 & 78.9 & 100 & 94.4 & 98.6 & 76.5 & 97.5 \\ RT-YOLO & 94.3 & 97.9 & 99.1 & 96.9 & 98.1 & 89.2 & 100 & 98.5 & 99.6 & 90.1 & 97.5 \\ Methods & \(mAP_{50}\)(\%) & Table & Dog & Horse & Moto & Person & Plant & Sheep & Sofia & Train & Tv \\ Faster-RCNN & 73.3 & 65.7 & 84.8 & 84.6 & 77.5 & 76.7 & 38.9 & 73.7 & 73.9 & 83.1 & 72.6 \\ SSD & 74.3 & 73.9 & 84.5 & 85.3 & 82.6 & 76.2 & 48.6 & 73.9 & 76 & 83.4 & 74 \\ YOLOv5-l & 82.2 & 59.7 & 91.4 & 83.1 & 90 & 85.8 & 54.4 & 68.8 & 79.1 & 91.2 & 71.1 \\ YOLOX-l & 84.4 & 64.0 & 88.4 & 86.2 & 90.9 & 88.2 & 63.1 & 69.2 & 77.95 & 95.5 & 80.3 \\ YOLOV7 & 89.2 & 75.2 & 91.8 & 91.4 & 93.3 & 89.4 & 57.6 & 82.5 & 88.9 & 98.2 & 88.1 \\ RT-YOLO & 94.3 & 88 & 96.2 & 93.9 & 98.4 & 96.2 & 78 & 89.5 & 92.7 & 97.8 & 94.5 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Generalization experiment results.  
Figure 16: Various categories of Map.  
network's focus to effective features with NAM. These components notably enhance the robustness and generalization performance of RT-YOLO.  
The generalization and robust performance of RT-YOLO provide practical applications value in current popular fields. In video surveillance [33], detecting objects at various scales and under occlusion is a significant challenge, and all subsequent tasks rely on successful object detection before region or behavior analysis. In the field of autonomous driving [34], detectors must be efficient and fast, demanding high performance, lightweight algorithms. RT-YOLO's performance serves as a reference for further research and lightweight performance improvements.