# A Real-Time Cotton Boll Disease Detection Model Based on Enhanced YOLOv11n
## 4 Results and Analyses
### Performance Comparison of Different Object Detection Models  
To evaluate the performance of the model proposed in this paper, several classic object detection models were selected for comparison, including the two-stage object detection model Faster R-CNN [25], the one-stage object detection algorithm CenterNet [26], and the MSA-DETR [27] model based on the Transformer architecture. In addition, YOLOv8-LSW [28] and DMN-YOLO [29] models specifically designed for small-target disease detection were also included in the comparison. All experiments were performed under identical datasets, training strategies, and test environments. The results are shown in Table 5.  
As shown in Table 5, the improved YOLOv11n-ECS model surpasses other comparative models in precision, recall, and mAP metrics. Compared to CenterNet, Faster R-CNN, YOLOv8-LSW, MSA-DETR, DMN-YOLO, and YOLOv11n, the proposed method achieves improvements of 25.7, 21.2, 5.5, 4.0, 4.5, and 2.3 percentage points in [EMAIL_ADDRESS], and 25.6, 25.3, 8.3, 2.8, 1.8, and 1.9 percentage points in [EMAIL_ADDRESS]:0.95, respectively. Regarding model size, YOLOv11n-ECS in 118.7 M, 102.7 M, 0.1 M, 33.6 M, 0.2 M, and 0.2 M smaller than CenterNet, Faster R-CNN, YOLOv8-LSW, MSA-DETR, DMN-YOLO, and YOLOv11n, respectively, demonstrating a significant lightweight advantage. As a two-stage object detection model, Faster R-CNN has a larger computational cost and parameter size, resulting in slower inference speed and a larger weight file. Its [EMAIL_ADDRESS] is only 64.4%, higher than only CenterNet. As representative single-stage object detection models, the YOLO series excels in both speed and detection performance. Compared to CenterNet, YOLOv8-LSW and YOLOv11n feature significantly smaller model sizes and lower computational costs, along with substantially higher mAP scores. The MSA-DETR model, built on the Transformer-based architecture, has achieved remarkable results in object detection tasks, with an [EMAIL_ADDRESS] score of 81.6%. This performance surpasses the YOLOv8-LSW and DMN-YOLO models, which were specifically optimized for disease detection tasks. However, MSA-DETR exhibits relatively high computational complexity and a large model size, ranking second only to CenterNet and Faster R-CNN in computational load and parameter count. Both the YOLOv8-LSW and DMN-YOLO models have undergone backbone network optimizations, enabling superior model lightweighting. Notably, DMN-YOLO demonstrates more robust localization capabilities, achieving an [EMAIL_ADDRESS]:0.95 of 60.9%, which surpasses the MSA-DETR by one percentage point. In contrast, the improved YOLOv11n-ECS model not only maintains its advantage in lightweight design but also achieves higher detection performance. In conclusion, the YOLOv11n-ECS model achieves an excellent balance between accuracy, speed, and lightweight design, making it suitable for real-time detection and resource-constrained scenarios. Compared to traditional detection models, it demonstrates stronger adaptability and robustness in small object detection, occlusion handling, and complex environments, making it highly applicable in a wide range of real-world scenarios.  
\begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline
**Models** & **Precision/\%** & **Recall/\%** & **[EMAIL_ADDRESS]\%** & **[EMAIL_ADDRESS]\%** & **FLOPs/G** & **Parameters/M** & **Model Size/MB** \\ \hline CenterNet & 62.6 & 58.2 & 59.9 & 37.1 & 70.2 & 32.66 & 124.0 \\ Faster & 50.7 & 60.9 & 64.4 & 37.4 & 369.8 & 136.7 & 108 \\ YOLOv8- & & 88.7 & 82.3 & 80.1 & 54.4 & 5.7 & 2.2 & 5.4 \\ LSW & 89.9 & 83.0 & 81.6 & 59.9 & 57.6 & 32.4 & 38.9 \\ DMN- & 87.5 & 80.3 & 81.1 & 60.9 & 6.7 & 2.9 & 5.5 \\ YOLOv11n & 90.1 & 81.9 & 83.3 & 60.8 & 6.4 & 2.5 & 5.5 \\ YOLOv11n- & 92.2 & 84.3 & 85.6 & 62.7 & 6.2 & 2.4 & 5.3 \\ ECS & & & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison of detection results of cotton ball diseases by different models.