## 3 Experimental Results and Analysis
### Analysis of Experimental Results  
#### 3.3.1 Ablation Experiment  
To verify the performance of the attention module proposed in this paper, we selected and compared the widely used SENet [14], ECA-Net [15], and SimAM [16] attention modules with the CBAM module used in this paper. By embedding them into the CenterNet backbone network, we obtained four models: CenterNet+SENet, CenterNet+ECA-Net, CenterNet+SimAM, and CenterNet+CBAM. As can be seen from the results in Table 4, the model in this paper has a higher recall and can detect more pests and diseases than the other attention modules. The average F1 score is also higher than that of the SENet, ECA-Net, and SimAM models, with improvements of 0.11, 0.10, and 0.09, respectively. The CBAM module focuses on both channel and spatial attention mechanisms, while SENet and ECA-Net focus on channel direction only, and SimAM is a 3D attention module that differs from existing channel and spatial attention modules. Compared with the CenterNet+SENet, CenterNet+SimAM and CenterNet+ECA-Net models, the proposed model improves the mAP by 0.44%, 1.41%, and 2.1%, respectively. This indicates that the CBAM module chosen in this paper can effectively calibrate the channel and spatial features, thus improving the detection performance of the algorithm. To further demonstrate the effectiveness of the modified module in the proposed algorithm, ablation experiments were conducted to compare the performance of the following four sets of models:  
1. In the existing CenterNet model, the backbone network is ResNet50, and the neck network is the deconvolution network layer, hereinafter referred to as ResNet50+ deconvolution layer;
2. The CBAM module is embedded in the backbone network of the original CenterNet model, hereinafter referred to as ResNet50+CBAM+deconvolution layer;
3. The backbone network is ResNet50, the new MFF module is added as the neck network, and the deconvolution layer of the original CenterNet model is deleted, hereinafter referred to as ResNet50+MFF; \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **P (\%)** & **R (\%)** & **F1 Score** & **mAP (\%)** \\ \hline CenterNet+SENet & 96.17 & 43.39 & 0.58 & 78.27 \\ CenterNet+ECA-Net & 94.87 & 44.71 & 0.59 & 76.97 \\ CenterNet+SimAM & 96.98 & 48.10 & 0.60 & 77.51 \\ CenterNet+CBAM & 91.39 & 61.03 & 0.69 & 78.62 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of results by different attention mechanisms. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Label Name** & **AP (\%)** & **P (\%)** & **R (\%)** & **F1 Score** \\ \hline Maize borer & 93.98 & 94.44 & 72.34 & 0.82 \\ Army worm & 91.50 & 92.31 & 75.00 & 0.83 \\ NLB & 86.60 & 89.55 & 64.52 & 0.75 \\ Red spider & 88.27 & 93.75 & 83.33 & 0.88 \\ Peach borer & 81.90 & 88.89 & 69.57 & 0.78 \\ Aphids & 87.69 & 90.60 & 67.95 & 0.78 \\ NLS & 76.35 & 96.25 & 24.06 & 0.39 \\ GLS & 74.73 & 93.75 & 25.62 & 0.40 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Model detection results in this paper. The algorithm in this paper is based on (3) to embed the CBAM module in the backbone network, hereinafter referred to as ResNet50+CBAM+MFF. As can be seen from the results in Table 5, the addition of CBAM module and MFF module significantly improves the model accuracy. Especially after adding the MFF module, the mAP of the ResNet50+MFF model improved by 6.96% compared to the original model. Moreover, the mAP of the ResNet50+CBAM+deconvolutional layer model with CBAM module embedded in the backbone network is 3.08 percent higher than that of the original model. This indicates that these two separate modules are very helpful for model detection. When the deconvolutional layer of the original model is removed after adding the MFF module, the number of parameters in the ResNet50+MFF model is reduced by 26.67%. Compared to the deconvolutional layer of the original model, the MFF module has a smaller parameter scale, and the number of parameters of the model is reduced, while the accuracy of the model is greatly improved.