# Multimodal Network for Object Detection Using Channel Adjustment and Multi-Scale Attention
## 4 Experiment
### Ablation  
To comprehensively evaluate the effectiveness of our proposed MNCM model, we conducted an ablation analysis focusing on its two key components: the dynamic channel adjustment (DCA) module and the multi-scale activated attention mechanism (MAAM) module. These modules are designed to enhance cross-modal feature representation and fusion, thereby improving overall model performance in classification and detection tasks. To assess their individual contributions, we systematically removed each module and analyzed the resulting performance variations. As shown in Tables 4 and 5, eliminating either component led to a noticeable decline in accuracy, underscoring their critical roles. Quantitative results demonstrate the effectiveness of both the DCA and MAAM modules. On the MSRS dataset, the DCA module improves mAP from 0.849 to 0.881 for Person \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{2}{c}{**AP50**} & \multicolumn{2}{c}{**AP75**} & \multicolumn{2}{c}{**mAP**} \\ \cline{2-7}  & **Person** & **Car** & **Person** & **Car** & **Person** & **Car** \\ \hline Faster R-CNN [31] & 0.831 & 0.853 & 0.804 & 0.736 & 0.769 & 0.683 \\ GNN-based [32] & 0.830 & 0.901 & 0.763 & 0.862 & 0.736 & 0.801 \\ CDDFuse [33] & 0.932 & 0.916 & 0.902 & 0.910 & 0.897 & 0.864 \\ ProbEn [34] & 0.906 & 0.913 & 0.842 & 0.869 & 0.811 & 0.844 \\ DETR [25] & 0.892 & 0.927 & 0.889 & 0.900 & 0.883 & 0.812 \\ SwinF [35] & 0.896 & **0.945** & 0.873 & 0.902 & 0.886 & 0.872 \\ TransFusion [36] & 0.915 & 0.928 & 0.903 & 0.901 & 0.891 & 0.884 \\ MMA-UNet [37] & 0.926 & 0.903 & 0.913 & 0.910 & **0.892** & 0.873 \\ \hline Ours & **0.941** & 0.939 & **0.933** & **0.920** & 0.881 & **0.894** \\ w/o DCA & 0.915 & 0.883 & 0.904 & 0.873 & 0.849 & 0.831 \\ w/o MAAM & 0.908 & 0.890 & 0.891 & 0.846 & 0.833 & 0.825 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Performance comparison on the MSRS dataset between our architecture, benchmark models, and variant architectures, which shows effectiveness in object detection under AP50, AP75, and mAP metrics. Figure 2: Qualitative comparison of detection results produced by CDDFuse, SWINF, MMAUNet, and our proposed method. detection (+3.2%) and from 0.831 to 0.894 for Car detection (+6.3%). Similarly, the MAAM module yields consistent gains, with Person mAP increasing from 0.833 to 0.881 (+4.8%) and Car from 0.825 to 0.894 (+6.9%). On the SAIC dataset, both modules generalize well, with DCA and MAAM achieving average AP50 improvements of 3.7% and 4.6%, respectively, across all object categories. These results highlight the complementary strengths of DCA and MAAM in enhancing spatial feature refinement and modality-aware representation. Specifically, the DCA module facilitates adaptive feature recalibration by dynamically adjusting channel-wise importance across modalities, ensuring that essential information is preserved during fusion. Meanwhile, the MAAM module expands the receptive field through dilated attention, allowing the model to capture modality-specific spatial dependencies and refine feature integration. Together, these modules work synergistically--DCA enhances deep modality interaction at the feature level, while MAAM strengthens spatial feature extraction through attention mechanisms--resulting in significant improvements in robustness and detection accuracy. These findings highlight the necessity of both components in the MNCM framework, demonstrating their complementary roles in optimizing the model's performance. In addition to evaluating the core modules (DCA and MAAM), we conducted systematic ablation studies on the loss function components to understand their individual contributions. To maintain model integrity during these experiments, we employed a _re-weighting_ strategy where the targeted loss term's weight (\(\lambda\)) was set to zero while keeping all network architectures intact.