# Residual Transformer YOLO for Detecting Multi-Scale Crowded Pedestrian
## 4 Experiments and Results
### Comparison Experiment  
In order to verify the detection and real-time performance of RT-YOLO algorithm, we compare it with the classical and state-of-the-art algorithms tested on CrowdHuman in terms of FPS, mAP, GFLOPS and number of parameters. Since the CrowdHuman training dataset is substantial, all algorithms do not use pre-trained weights. Each algorithm is trained for 200 epochs with input size of 640 \(\times\) 640, and the best training weights are used  
Figure 12: Deleted annotations in WiderPerson.  
to calculate results on the CrowdHuman test set. The results are shown in Table 2 and Figure 13.  
In Table 2, The RT-YOLO includes extra calculation modules, resulting in slightly lower speed compared to YOLOv7, similar to the state-of-the-art YOLOv8-l. However, it still reaches 67 FPS, ensuring real-time detection, with a 3.8% improved \(mAP_{50}\) and 4.9% improved \(mAP_{50}\). In terms of model complexity, RT-YOLO has 46.1 fewer GFLOPS and 4.1 million fewer parameters compared to the latest YOLOv8-l. Compared to YOLOv7, both the parameter count and complexity increased, but it achieved a significant improvement in accuracy. Figure 13 shows the PR curves enclosed by Precision and Recall. RT-YOLO stands out with the highest Recall value, indicating the lowest rate of missed detections for pedestrian objects, surpassing other algorithms. In addition, RT-YOLO is deployed on RTX 2080 Ti with Tensorrt FP32 precision acceleration. With a slight reduction in mAP, the FPS has increased to 84, still maintaining its practical value.  
The YOLOv7, YOLOv8, and RT-YOLO, which perform well in experiments, are applied to real-life scenes and compared for detection effects. The detection results are shown in Figure 14. From the detection results, it can be seen that RT-YOLO excels in practical applications, particularly in multi-scale feature fusion, and enhances the detection of small-scale and occluded objects. In Figure 14I, RT-YOLO successfully detects distant pedestrian objects that are missed by YOLOv7 and YOLOv8-l.; In Figure 14 II, RT-YOLO successfully detects crowded pedestrians in the upper part of the image that are missed by YOLOv7 and YOLOv8-l; In Figure 14 III, where the objects in the image are blurred and difficult to identify, RT-YOLO successfully detects a greater number of distant and near-field pedestrian objects.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Methods** & **Params** & **GFLOPS** & \(mAP_{50}\)**(\%)** & \(mAP_{50,95}\)**(\%)** & **FPS** \\ \hline SSD [30] & 34.3 M & 386.2 & 69.6 & 35.5 & 41 \\ RetinaNet [31] & 45.7 M & 218.3 & 76.1 & 45.7 & 50 \\ Faster-RCNN [9] & 41.5 M & 207.1 & 79.3 & 46.6 & 52 \\ SOLIDER [32] & 43.9 M & 259.5 & 82.2 & - & 19 \\ YOLOv5-l & 46.5 M & 109.1 & 80.2 & 50.4 & 71 \\ YOLOv1 [18] & 54.2 M & 155.6 & 81.0 & 49.0 & 45 \\ YOLOv7 & 37.6 M & 106.5 & 83.1 & 52.3 & 81 \\ YOLOv8-l & 43.7 M & 165.2 & 86.3 & 56.5 & 68 \\ RT-YOLO (ours) & 39.6 M & 119.1 & 86.9 & 57.2 & 67 \\ RT-YOLO (Tensorrt) & - & - & 86.2 & 55.9 & 84 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Algorithm Comparison Results.  
Figure 13: The comparison of algorithm parameters.  
In summary, RT-YOLO effectively distinguishes occluded pedestrian boundaries, preserves features of small-scale pedestrians through multi-scale fusion, significantly enhances the detection accuracy of crowded pedestrians, and maintains real-time performance.