## 3 Experimental Results and Analysis
### Analysis of Experimental Results
#### 3.3.2 Comparative Analysis Experiment  
The detection results of the proposed model and the original CenterNet model on the test set are compared and analyzed. In the detection results, all detected objects are marked with a border, and the label name and confidence level of the object are displayed. It can be seen from Figure 8 that the original model misses detection when the infestation is clustered, and from Figure 9, it can be seen that the original model misdetects the GLS as the NLS at the same time. The improved model uses an attention mechanism to suppress interfering objects, integrates multi-scale features, improves the detection performance of objects, effectively detects object outcomes, and has higher confidence in prediction results. To further evaluate the performance of this model, a comparison experiment was conducted using mainstream object detection algorithms. The dataset used in the experiments was the maize leaf pests and diseases dataset from this paper, and the same experimental parameters and experimental environment were set. Disease and insect detection were performed between the proposed model and the Faster R-CNN [17], YOLOv5, SSD-VGG [18], and Efficientdet-D0 [19] models. Table 6 and Figure 10 show that compared with SSD-VGG, YOLOv5, Faster R-CNN, and Efficientdet-D0 methods, the mean average precision (mAP) of the proposed model was the highest among the five detection algorithms, increasing by 9.17%, 7.34%, 4.43%, and 2.46%, respectively. This demonstrates the effectiveness of the proposed method. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **P (\%)** & **R (\%)** & **F1 Score** & **mAP (\%)** & **Parameters** (M)** \\ \hline ResNet50+Deconvolutional (Original) & 95.36 & 32.01 & 0.45 & 75.54 & 32.665 \\ ResNet50+CBAM+Deconvolutional & 91.39 & 61.03 & 0.69 & 78.62 & 33.714 \\ ResNet50+MFF & 91.21 & 57.97 & 0.68 & 82.50 & 23.952 \\ ResNet50+CBAM+MFF (Ours) & 92.11 & 60.30 & 0.70 & 85.13 & 24.296 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Ablation experiments based on improved CenterNet model. Figure 8: Comparison of detection results for leakage cases. (**a**) Original CenterNet model; (**b**) our model. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Model** & **P (\%)** & **R (\%)** & **F1 Score** & **mAP (\%)** &
\begin{tabular}{c} **Parameters** \\ **(M)** \\ \end{tabular} & **FPS (f/s)** \\ \hline SSD-VGG & 78.63 & 65.82 & 0.68 & 74.17 & 26.285 & 58.13 \\ CenterNet & 95.36 & 32.01 & 0.45 & 75.54 & 32.665 & 57.60 \\ Faster R-CNN & 54.10 & 82.55 & 0.64 & 76.00 & 137.099 & 12.29 \\ YOLOv5 & 90.83 & 45.98 & 0.59 & 78.91 & 47.057 & 48.18 \\ EfficientDet-D0 & 87.23 & 71.62 & 0.76 & 82.67 & 3.874 & 14.63 \\ Ours & 92.11 & 60.30 & 0.70 & 85.13 & 24.296 & 23.69 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Pest detection results of different object detection models. Figure 9: Comparison of detection results in case of misdetection. (**a**) Original CenterNet model; (**b**) our model. The SSD-VGG algorithm is a one-stage algorithm. It first performs dense sampling uniformly at different locations on the image and then uses convolutional neural networks for feature extraction, followed by classification and regression. The SSD-VGG algorithm has the advantage of its fast speed, which is the fastest among the six algorithms, but its detection accuracy is insufficient. Compared to the SSD-VGG algorithm, Faster R-CNN has a higher detection accuracy, but the parameter scale is large due to its two-stage algorithm. The YOLOv5 algorithm improves the detection accuracy and parameter scaling to some extent, but its detection performance is poor on this dataset. The Efficientdet-D0 algorithm has the smallest parameter size among the six algorithms, but it uses more convolution operations, resulting in slower model detection.