## 4 Results
### Comparison Experiment
#### 4.1.2 Comparative Experiments Between Different Mainstream Models  
In this section, in order to verify the effectiveness of our proposed Slim-YOLO model in the task of sugarcane tail tip target detection in complex environments, we conducted comparative experiments between it and the current mainstream target detection models. The comparison models mainly include RTDETR-I, YOLOv8n, YOLOv9t, YOLOv10n, YOLO11n, YOLO11s, YOLO11m, and YOLO11l. The comparison results are shown in Table 2. The results of the visualization experiment are shown in Figure 12.  
The experimental results show that our proposed Slim-YOLO achieves optimal performance in both key detection accuracy metrics, mAP50 and mAP50-95, reaching 0.922 and 0.482, respectively, which is slightly improved compared to RTDETR-I (0.916 and 0.435), but the number of parameters of Slim-YOLO is only 1.33M, which is 95.8% less compared to the number of parameters of RTDETR-I (31.99M), which is reduced by 95.8%, indicating that our model is more lightweight and has lower computational and storage costs while achieving similar or even higher detection accuracy. Compared with the lightweight models YOLOv8n, YOLOv9t, YOLOv10n, and YOLO11n, Slim-YOLO performs better in all the metrics, especially in mAP50-95, which is 6.1% better than YOLO11n, and 7% better than YOLOv9t, and in terms of computational complexity, Slim-YOLO's GFLOPs is only 3.5, which is 44.4% less than YOLO11n (6.3) and 56.8% less than YOLOv8n (8.1), further proving its efficiency. Compared to the larger-scale YOLO11s, YOLO11m, and YOLO11l,  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Model** & **P** & **R** & **mAP50** & **mAP50-95** & **GFLOPS** & **Params** & **FPS** & **Model\_Size/M** \\ \hline RTDETR-I [21] & 0.859 & 0.871 & 0.916 & 0.435 & 103.4 & 31,985,795 & 39.0 & 63.1 \\ YOLOv8n [22] & 0.790 & 0.845 & 0.837 & 0.419 & 8.1 & 3,005,843 & 243.3 & 6.0 \\ YOLOv9t [23] & 0.811 & 0.828 & 0.836 & 0.412 & 7.6 & 1,970,979 & 92.4 & 4.4 \\ YOLOv10n [24] & 0.778 & 0.810 & 0.817 & 0.400 & 8.2 & 2,694,806 & 150.5 & 5.5 \\ YOLO11n [12] & 0.808 & 0.835 & 0.840 & 0.421 & 6.3 & 2,582,347 & 176.0 & 5.2 \\ YOLO11s [12] & 0.838 & 0.899 & 0.911 & 0.485 & 21.3 & 9,413,187 & 171.1 & 18.3 \\ YOLO11m [12] & 0.830 & 0.890 & 0.906 & 0.483 & 67.6 & 20,030,803 & 137.9 & 38.6 \\ YOLO11l [12] & 0.819 & 0.888 & 0.891 & 0.471 & 86.6 & 25,280,083 & 93.7 & 48.8 \\
**Slim-YOLO (ours)** & **0.853** & **0.856** & **0.922** & **0.482** & **3.5** & **1,331,835** & **189.1** & **3.0** \\ \hline \hline \end{tabular}
\end{table}
Table 2: Direct comparative experimental results of different mainstream models.  
Figure 12: Comparison of mAP50 visualizations for each model.  
Slim-YOLO still achieves an advantage in accuracy, i.e., 1.6% over YOLO11m and 3.1% over YOLO111 in mAP50, while the number of parameters is only 14.1% of that of YOLO11s (9.41M), YOLO11m (20.03M), 6.6% of YOLO11m (20.03M), and 5.3% of YOLO111 (25.28M), which significantly reduces the computational requirements. From the perspective of Frames Per Second (FPS), the proposed Slim-YOLO demonstrates outstanding inference speed advantages. Compared to YOLO11n (176 FPS), YOLO11s (171.1 FPS), and YOLO11m (137.9 FPS), Slim-YOLO achieves an increase of 7.4%, 10.5%, and 37.1%, respectively, with an inference speed of 189.1 FPS. The optimized model has a compact size of only 3.0 MB and is capable of processing 189 images per second, making it well-suited for deployment in complex sugarcane field environments. Overall, Slim-YOLO achieves significant lightweight and computational efficiency optimization while maintaining high detection accuracy and demonstrates extremely high comprehensive performance and application value compared to other mainstream models. The results of the visualization comparison of the different models are shown in Figure 13.