## 3 Experimental Results
### Ablation Experiments  
To assess the impact of the proposed improvements on the performance of the original algorithm, we integrated the improved mechanisms into the baseline algorithm one by one and performed detailed experimental comparisons on each integrated algorithm configuration using the same test set. This aims to comprehensively understand the contribution of the proposed mechanisms to the overall performance of the algorithm. The experimental results are detailed in Table 1.  
From the table, it can be observed that the performance of the original algorithm improves significantly when three different modules are added. The wide field of view convolutional backbone network (WFVC Net) employs larger convolutional kernels to enhance the network's perception and generalization capabilities. Larger convolutional kernels can cover a broader range of the input image, allowing each neuron to sense a wider array of local information. This enables the model to better capture shape information in images, enhancing its representational ability, and making it more effective in  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Baseline** & **WFVC Net** & **ODC2\(\ell\)** & **EM-DCA** & **WIoU** & **Param** & **GFLOPs** & **P** & **R** & **mAP50** \\ \hline \(\surd\) & & & & & & 43.608 & 164.8 & 98.66 & 91.87 & 96.23 \\ \(\surd\) & \(\surd\) & & & & 45.452 & 153.4 & 96.97 & 92.46 & 96.91 \\ \(\surd\) & & & \(\surd\) & & & 43.887 & 128.3 & 98.88 & 94.60 & 98.10 \\ \(\surd\) & & & \(\surd\) & & 43.887 & 128.3 & 95.54 & 94.44 & 96.87 \\ \(\surd\) & \(\surd\) & \(\surd\) & & & 45.726 & 116.9 & 98.97 & 93.74 & 97.91 \\ \(\surd\) & \(\surd\) & & \(\surd\) & & 61.502 & 246.1 & 94.72 & 90.52 & 94.69 \\ \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) & 45.835 & 117.1 & 99.42 & 95.13 & 99.01 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Ablation study results.  
detecting complex scenes. Compared to the baseline model, the missing detection rate has significantly improved, with both recall and mAP values showing enhancements.  
The ODC2F module for full-dimensional feature fusion learns attention features across the four dimensions of the convolution kernels through a parallel strategy, providing better performance in capturing rich contextual information, leading to a qualitative improvement in the model's evaluation metrics. Furthermore, the Expectation Maximization Dynamic Convolutional Attention (EM-DCA) mechanism helps the model to more effectively capture critical information in the input data while suppressing irrelevant complex background information. Compared to the original algorithm, the improvements are evident, with the model's mAP increasing by 2.78% and the recall value increasing by 3.26%.  
From the table data, it is clear that integrating the three different modules into the original algorithm significantly enhances performance. The wide field of view convolutional backbone network strengthens the model's representational ability by employing larger convolutional kernels, allowing each neuron to sense a broader array of local information. Compared to the baseline model, this approach notably reduces the missing detection rate and achieves improvements in both recall and mean average precision (mAP).  
Additionally, the ODC2F module for full-dimensional feature fusion learns across the four dimensions of the convolution kernels using a parallel strategy, enabling the model to more effectively capture rich contextual information, with a 1.87% increase in recall. The EM-DCA mechanism further boosts model performance by more effectively identifying critical information in the input data