## 5 Results
### Ablation Experiment  
To verify the effect of different improvement modules on model performance, we designed an ablation experiment. By gradually introducing different improvement modules, we assessed the impact of each module on model performance. These improvement modules include replacing the backbone part with the EfficientNet_B0 feature extraction network, improving the Neck to a CCFM structure, improving the C2f module to an F-C2f, replacing the original loss function with Focal-EIoU, and adopting a RepHead detection head. In the ablation experiment, these improvement modules were introduced one by one to observe changes in model performance and explore the contribution of each improvement module to model performance, providing guidance and reference for model optimization. The results are shown in Table 4. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & **Backbone Network** & **P/\%** & **R/\%** & **[EMAIL_ADDRESS]/\%** & **Params/10\({}^{6}\)** & **GFLOPs/G** & **FPS/(frame\(\cdot\)s\({}^{-1}\))** \\ \hline \multirow{8}{*}{
\begin{tabular}{c} Vision \\ Transformers \\ \end{tabular} } & CSPDarknet (Baseline) & 92.9 & 88.2 & 93.1 & 11.14 & 28.8 & 98.04 \\ \cline{2-7}  & RepViT-M0\_9 [45] & 92.0 & 85.6 & 92.0 & 8.57 & 22.7 & 80.64 \\  & EfficientViT\_M0 [46] & 91.0 & 83.6 & 90.0 & 8.38 & 20.4 & 56.75 \\  & EfficientViT\_M1 [46] & 93.0 & 83.9 & 91.0 & 9.03 & 25.1 & 57.69 \\  & MobileViTv1-XXS [47] & 92.4 & 86.3 & 92.2 & 7.64 & 20.9 & 76.92 \\  & MobileViTv2-XXS [48] & 92.9 & 86.7 & 92.6 & 7.71 & 22.4 & 81.56 \\ \hline \multirow{8}{*}{ConvNets} & Mobilenetv3\_small [49] & 90.1 & 84.8 & 89.7 & 6.73 & 16.7 & 90.91 \\  & ShuffleNetv2 [50] & 91.4 & 83.5 & 89.3 & 5.94 & 16.0 & 105.65 \\ \cline{1-1}  & VanillaNet-5 [51] & 91.4 & 80.6 & 88.5 & 6.36 & 18.3 & 117.65 \\ \cline{1-1}  & FasterNet [42] & 89.3 & 85.2 & 90.2 & 6.08 & 16.1 & 114.94 \\ \cline{1-1}  & GhostNetV2 [52] & 92.9 & 86.0 & 92.0 & 8.24 & 19.1 & 75.56 \\ \hline ConvNets & Efficientnet\_B0 & 93.5 & 87.5 & 92.8 & 6.53 & 17.3 & 96.15 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Backbone network comparison experiment results. From the ablation study results in Table 4, it can be seen that after replacing the original model's backbone network with EfficientNet_B0, the model's mAP50 and detection speed slightly decreased, but the parameter count and computational load were reduced by 41.38% and 39.93%, respectively. This demonstrates the effectiveness of the improved module in achieving a lightweight model. After improving the Neck part to a CCFM structure, the model's parameter count was reduced by 34.83%, the computational load was reduced by 19.44%, the detection speed increased by 9.68%, and the mAP50 improved by 0.11%. The overall performance of the model was significantly enhanced, demonstrating the effectiveness of the CCFM structure. After improving the original model's C2f module to the F-C2f module with PConv, the model's parameter count decreased by 31.87%, the computational load decreased by 31.59%, the detection speed increased by 6.25%, and the mAP50 improved by 0.75%. The overall performance of the model was significantly enhanced, demonstrating the effectiveness of the F-C2f module. When the loss function of the original model was replaced with Focal-EIoU, mAP50 increased by 1.29%, and detection speed improved by 21.43%, while maintaining the same number of parameters and computational load. This indicates that Focal-EIoU can effectively enhance the model's detection accuracy and speed. When the RepHead module is added to the original model, although the number of parameters and computational load increase, both the mAP50 and detection speed improve. Notably, when the CCFM module and RepHead module are added together to the original model, the model's parameter count and computational load decrease more compared to adding the CCFM module alone, and both the mAP50 and detection speed are significantly improved.