# Residual Transformer YOLO for Detecting Multi-Scale Crowded Pedestrian
## 4 Experiments and Results
### Ablation Experiment
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Methods** & **Layers** & **Params** & **GFLOPS** & **FPS** \\ \hline YOLOv7 & 415 & 37.6 M & 106.5 & 81 \\ YOLOv7 + multi-scale & 509 & 37.8 M & 119.5 & 65 \\ Previous + 2 \(\times\) BTDetect & 535 & 38.0 M & 119.0 & 68 \\ RT-YOLO (previous + NAM) & 550 & 39.6 M & 119.1 & 67 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: RT-YOLO network parameters. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Methods** & **F1** & \(mAP_{50}\)**(\%)** & \(mAP_{50,95}\)**(\%)** \\ \hline YOLOv7 & 0.72 & 76.7 & 45.6 \\ YOLOv7 + multi-scale & 0.74 & 78.9 (+2.9) & 48.1 (+2.5) \\ Previous + 2 \(\times\) BTDetect & 0.76 & 79.6 (+0.7) & 49.4 (+1.3) \\ RT-YOLO (previous + NAM) & 0.78 & 80.1 (+0.5) & 49.6 (+0.2) \\ \hline \hline \end{tabular}
\end{table} 
Table 5: WiderPerson experimental results. information and shallow-level details, improving the efficiency of detecting small-scale and occluded pedestrians. The additional detection layer increases the overall depth and complexity of the network but brings a significant mAP improvement and provides the basis for BTDetect, so it is acceptable. * Effect of BTDetect prediction head: The two large-scale feature maps output by Neck are passed into BTDetect. The Bottleneck residual structure of BTDetect can alleviate the issues of gradient disappearance and feature information loss. Additionally, the Transformer efficiently captures detailed information from the larger-scale feature maps, focusing on small-scale and obscured object features. This effectively enhances the detection performance. Ultimately, BTDetect divides the feature map into grids and decouples within each cell to predict object positions and categories. The overall \(mAP_{50}\) improvement is 0.8% in the CrowdHuman and 0.7% in the WiderPerson. The \(mAP_{5095}\) improvement in CrowdHuman is 1.2%, and in WiderPerson, it is 1.3%. * Effect of NAM attention: NAM Attention is a lightweight, plug-and-play module without increasing complexity. The NAM attention in RT-YOLO computes weighted attention on images from channel and space, which reduces the influence of irrelevant regions in the feature map. The overall \(mAP_{50}\) improvement is 0.3% in the CrowdHuman and 0.5% in the WiderPerson. The \(mAP_{5095}\) improvement in CrowdHuman is 0.4%, and in WiderPerson, it is 0.2%. As shown in Figure 15, YOLOv7 has some degree of missed detection in various complex scenes. RT-YOLO is able to fully extract object feature information, achieving accurate localization and classification of small-scale, obscured objects. Figure 15: The detection results. In summary, RT-YOLO increases the depth of the network within the acceptable range of computational and complexity increments. This enhances the high-level features with richer semantic information, while the multi-scale fusion training effectively fuses rich details from low-level features into feature maps, compensating for the loss of detailed features caused by the deep network layers and thereby improving the algorithm's feature extraction capability.