## 3 Experimental and Analysis
### Ablation Study
#### 3.4.2 Ablation Experiments on the Dilation Rate Parameters of the ERM Module  
To evaluate the impact of the dilation rate parameters of the ERM module on model performance and computational complexity, three different parameter combinations for the ERM module were set up for ablation comparison. The experimental results are shown in Table 4. It was observed that the ERM modules with different dilation rate parameter combinations effectively improved the detection accuracy and had a minimal impact on the model's computational load. Among them, the ERM module with a dilation rate combination of (1,3,3,5) achieved the best detection performance with a detection accuracy of 96.2%, while maintaining a low computational load and number of parameters. Therefore, this dilation rate parameter setting was used as the benchmark in this study. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Models**} & \multirow{2}{*}{**mAP/\%**} & \multicolumn{4}{c}{**AP/\%**} \\ \cline{3-6}  & & **Truck** & **Excavator** & **Crane** & **Loader** \\ \hline YOLOv5s & 95.4 & 92.3 & 95.5 & 97.6 & 96.1 \\ CER-YOLOv5s & 98.3 & 97.1 & 98.2 & 99.1 & 98.7 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of model detection performance. Figure 12: The performance parameter curves: (**a**) mAP curves; (**b**) loss curves. Experiment 2 involved adding only the CGC3 module, which increased the mAP by 1.7% compared with the original model, reaching a final mAP of 97.1%. The parameter count was reduced to 70.1% of the original model, demonstrating that the Ghost bottleneck structure in the CGC3 module effectively reduced the model complexity. Additionally, the integrated CBAM attention mechanism adaptively extracted key features, enhancing detection performance. Experiment 3 involved replacing the original feature pyramid network with the E-BiFPN structure. This approach, by fully integrating shallow and deep information, improved the efficiency of multi-scale feature fusion. Although this led to a slight increase in complexity compared with the original model, the mAP improved by 2%. Experiment 4 involved adding the ERM (enhanced receptive field module) to the neck of the network. By using dilated convolutions with different dilation rates to capture multi-scale contextual relationships of targets, this module effectively improved the detection accuracy for small targets. Despite a slight increase in model parameters, the mAP increased by 0.8%. Experiment 5 aimed to address the issue of the traditional Soft-NMS algorithm's insensitivity to distance measurement by proposing the Soft-DIoU-NMS algorithm. This new approach reduced incorrect suppression of occluded targets, resulting in a 1.3% improvement in mAP. From the perspective of combining improvement modules, Experiment 6, which added E-BiFPN to the setup from Experiment 2, further enhanced the model's feature extraction capability. This resulted in an additional 0.3% increase in mAP. Experiment 7 built on Experiment 6 by adding the ERM module, which expanded the receptive field of shallow features. This addition led to a 0.5% increase in mAP. Experiment 8 involved simultaneously incorporating all four improvement modules into the model. The final mAP reached 98.3%, with the model's parameter count and computational complexity reduced to 5.08 M and 10.9 G, respectively. This further demonstrated the effectiveness of the proposed improvement strategies in the target detection task. \begin{table}
\begin{tabular}{c c c c} \hline
**Models** & **mAP/\% \(\uparrow\)** & **Params/M \(\downarrow\)** & **GFLOPs \(\downarrow\)** \\ \hline YOLOv5s & 95.4 & 7.03 & 16.0 \\ YOLOv5s + ERM (r1 = 1, r2 = 3, r3 = 3, r4 = 5) & 96.2 & 7.11 & 16.2 \\ YOLOv5s + ERM (r1 = 3, r2 = 3, r3 = 3, r4 = 5) & 95.9 & 7.13 & 15.5 \\ YOLOv5s + ERM (r1 = 3, r2 = 5, r3 = 5, r4 = 7) & 95.5 & 7.22 & 16.2 \\ \hline \end{tabular}
\end{table} 
Table 4: Ablation study on the dilation rate parameter.