# Residual Transformer YOLO for Detecting Multi-Scale Crowded Pedestrian
## 4 Experiments and Results
### Ablation Experiment  
In order to verify the performance of the improved components, combinations of each part are trained in ablation experiments. Subsequently, they are tested on the CrowdHuman and WiderPerson datasets. In the table, higher GFLOPS indicates greater model complexity. FPS stands for the detection speed of the algorithm. Params represents the model size, and Layers refers to network depth. F1 score ranges from 0 to 1 and is used to measure the classification ability. Analyzing Tables 3-5, the RT-YOLO compared to the original algorithm, exhibits an increase in model complexity of 12.6 GFLOPS and an addition of 2 million parameters. This leads to a reduction in speed by 24 FPS. Notably, in CrowdHuman, the overall \(mAP_{50}\) improves by 3.8%, and \(mAP_{50:95}\) improves by 5.1%. In WiderPerson, it achieves a 3.4% overall \(mAP_{50}\) improvement and a 4% improvement in \(mAP_{50:95}\). The F1 score also improves on both datasets. In summary, RT-YOLO increases model complexity but significantly improves detection performance and classification performance. Figure 14: RT-YOLO and classical algorithm detection effect comparison. In the CrowdHuman dataset, most person labels have dimensions ranging from approximately 50 \(\times\) 100 to 150 \(\times\) 400, while head labels have dimensions between 20 \(\times\) 20 and 150 \(\times\) 200. These represent two distinct object scales, with person labels addressing challenges related to occlusions, and head labels addressing challenges related to smaller-scale objects. According to the results presented in Table 6, RT-YOLO exhibits significant improvements in detecting both label categories. In head category, the overall \(mAP_{50}\) improves by 3.5%, and \(mAP_{50.95}\) improves by 4.5%. In person category, there is an overall 4.1% improvement in \(mAP_{50}\) and a 5.3% improvement in \(mAP_{50.95}\). Considering the above tables, we analyze the effects of different components on the performance of RT-YOLO from three aspects:  
* Effect of multi-scale fusion strategy with BOTrans: The addition of a BOTrans object detection layer increases the network layers from 415 to 509. GFLOPS increase from 106.5 to 119.5. The FPS has decreased from 81 to 67, but it still maintains real-time requirements. In CrowdHuman, the overall \(mAP_{50}\) improves by 2.7%. The \(mAP_{50.95}\), which better reflects overall performance, improves by 3.5%. In WiderPerson, the improvements are 2.2% and 2.5%, respectively. In the BOTrans detection layer, self-attention efficiently distinguishes occluded pedestrian boundaries, reducing miss and false detection. During model training, the combination of shallow-level details with deep semantic information, alleviating the issue of effective feature loss due to increasing network depth. The resulting feature maps contain high-level semantic \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multicolumn{2}{c}{\(mAP_{50}\)**(\%)**} & \multicolumn{2}{c}{\(mAP_{50,95}\)**(\%)**} \\ \cline{2-5}  & **Head** & **Person** & **Head** & **Person** \\ \hline YOLOv7 & 84.2 & 81.9 & 51.7 & 52.6 \\ YOLOv7 + multi-scale & 86.7 (+2.5) & 84.8 (+2.9) & 54.8 (+3.1) & 56.0 (+3.4) \\ Previous + 2 \(\times\) BTDetect & 87.5 (+0.8) & 85.8 (+1.0) & 56.0 (+1.2) & 57.6 (+2.6) \\ RT-YOLO (previous + NAM) & 87.7 (+0.2) & 86.0 (+0.2) & 56.2 (+0.2) & 57.9 (+0.3) \\ \hline \hline \end{tabular}
\end{table} 
Table 6: CrowdHuman experimental results for different scale objects. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Methods** & **F1** & \(mAP_{50}\)**(\%)** & \(mAP_{50,95}\)**(\%)** \\ \hline YOLOv7 & 0.80 & 83.1 & 52.3 \\ YOLOv7 + multi-scale & 0.83 & 85.8 (+2.7) & 55.8 (+3.5) \\ Previous + 2 \(\times\) BTDetect & 0.85 & 86.6 (+0.8) & 57.0 (+1.2) \\ RT-YOLO (previous + NAM) & 0.85 & 86.9 (+0.3) & 57.4 (+0.4) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: CrowdHuman experimental results.