## 4 Experiment and Evaluations
### Experimental Results  
#### 4.4.1 Comparative Experiment  
In order to comprehensively and objectively evaluate the improved RT-DETR algorithm, this study conducts experimental comparisons on four different algorithms and their corresponding versions with different network structures. Precision, Recall, mAP50, and mAP50:95, etc., were chosen as evaluation metrics. The tested algorithm models include the traditional YOLOv5s, YOLOv7-tiny, YOLOv7, YOLOv7-X, YOLOv8, YOLOv8-P2, YOLOv8-P6, and the improved RT-DETR proposed in this study. To guarantee the accuracy of the comparative experiments, all models are trained without loading pre-trained weights. The results presented in Table 2 indicate that the proposed improved algorithm surpasses the current methods across most evaluation metrics. Figure 8 illustrates the disease detection performance of various algorithm models. Among them, Figure 8c,f,h have missed detection of tea brown blight in the top left corner of the image, and Figure 8b,c have missed detection of tea brown blight in the middle of the image. Moreover, in terms of detection details, only Figure 8b,h,i detected part of the tea brown blight on the left part of the image, and the rest were missed. In addition, the detection anchor box in Figure 8c does not fully fit the disease, and the disease is not fully detected. In comparison, the model introduced in this study has higher detection \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Number** & **Model** & **Precision (\%)** & \begin{tabular}{c} **Recall** \\ **(\%)** \\ \end{tabular} & \begin{tabular}{c} **mAP50** \\ **(\%)** \\ \end{tabular} & \
\begin{tabular}{c} **mAP50** \\ **(\%)** \\ \end{tabular} &
\begin{tabular}{c} **mAP50**-95 \\ **(\%)** \\ \end{tabular} \\ \hline
0 & YOLOv5s & 88.63 & 80.88 & 85.81 & 51.62 \\
1 &
\begin{tabular}{c} YOLOv7- \\ tiny \\ \end{tabular} & 83.61 & 77.59 & 81.28 & 42.96 \\
2 & YOLOv7 & 88.78 & 83.17 & 86.94 & 59.27 \\
3 & YOLOv7-X & 90.26 & 82.15 & 87.36 & 60.43 \\
4 & YOLOv8 & 81.48 & 74.45 & 80.17 & 48.92 \\
5 & YOLOv8-P2 & 80.86 & 75.18 & 80.27 & 49.26 \\
6 & YOLOv8-P6 & 83.54 & 74.96 & 81.38 & 51.58 \\
7 & Ours & 89.20 & 81.90 & 85.80 & 59.30 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison results with other methods. quality on the same detection image, can comprehensively detect the disease, the detection anchor box also fits the disease area better, and can maintain a high detection level. This indicates that the model presented in this study can detect diseases with greater accuracy and comprehensiveness. To further evaluate the effectiveness and robustness of the improved RT-DETR algorithm, Grad-CAM++ was employed to generate heatmaps, visually highlighting the regions of interest detected by various network backbones. Heatmaps act as a valuable tool for visualizing how models concentrate on particular areas during detection, offering important insights into their capability to localize and identify disease-affected regions. Figure 9 presents the heatmaps for detecting tea diseases, generated by the proposed backbone compared to other backbones, including RT-DETR-R18 [37], ConvNextV2 [38], LSKNet [39], EfficientViT [34], RepViT [40], EfficientFormerV2 [41], and UniRepLKNet [42]. Figure 8: _Cont._  
Figure 8: The performance of various algorithmic models in disease detection. Figure 9: Heat map of detection results of different network backbones. Compared with previous models, the proposed model exhibits a slight decline in precision and recall; however, it demonstrates significant advantages in terms of lightweight performance. Specifically, the model achieves substantial reductions in FLOPs, remarkable improvements in FPS, a significant decrease in the number of parameters, and a notable compression in model size. These improvements effectively address the limitations of traditional models, such as slow detection speed and excessive model size, providing a more efficient solution for scenarios with constrained computational resources and high real-time requirements.