# Multimodal Network for Object Detection Using Channel Adjustment and Multi-Scale Attention
## 4 Experiment
### Comparison Experiment and Ablation  
We selected several methods to conduct experiments on the MSRS dataset and the SAIC dataset. The experimental results validating the effectiveness of image fusion are presented in Tables 2 and 3. The results of the objection detection are presented in Tables 4 and 5. * **Fast R-CNN**[31]: A domain adaptive object detection method based on Faster R-CNN that mitigates image-level and instance-level shifts using adversarial training and consistency regularization. * **GNN-based**[32]: A joint multi-object tracking (MOT) approach based on Graph Neural Networks (GNNs) that simultaneously optimizes object detection and data association by modeling spatial and temporal relationships. * **CDDFuse**[33]: CDDFuse is a multimodal image fusion method that employs a dual-branch architecture combining Transformer and CNN components
* **ProbEn**[34]: A multimodal object detection method based on probabilistic ensembling to effectively integrate information from multiple sensor modalities. * **DETR**[25]: An end-to-end object detection framework that uses a transformer encoder-decoder architecture with bipartite matching loss to directly predict object sets. * **SwinF**[35]: A feature fusion network based on Swin Transformer, designed to enhance object detection performance while reducing computational complexity through hierarchical windowing operations. * **TransFusion**[36]: A unified multimodal model that combines next-token prediction and diffusion processes in a single transformer to jointly model continuous data. * **MMA-UNet**[37]: A multimodal asymmetric UNet designed for balanced feature fusion by employing specialized encoders and cross-scale fusion strategies. In the evaluation of image fusion, we employed six metrics to conduct a quantitative analysis of the results, which are presented in Tables 1 and 2. The results show that our method outperforms the comparison methods in nearly all metrics. Specifically, higher VIF and PSNR values indicate greater fidelity between the source images and the fused image, meaning that the fused image better resembles the original source images. Higher Qabf and SSIM values suggest that the fused image retains more information from the source images with minimal distortion. The EN value reflects the preservation of image details and texture information, with higher EN values indicating that more details and texture are retained. Our method surpasses the comparison methods in almost all metrics, particularly excelling in key metrics like VIF and PSNR. Compared with traditional neural network-based algorithms (such as CNN and Fast R-CNN), our method shows significant improvement. When compared to transformer-based methods (such as SwinF and CDDFuse), our approach also performs better in most metrics, likely due to the dynamic channel adjustment strategy we proposed, which enhances deep interaction between modalities. Additionally, our method outperforms newer techniques such as ProbEn and the asymmetric network MMA-UNet in most metrics, demonstrating its advantage in handling complex image fusion tasks. Overall, our method, with its innovative strategies and modules, achieves significant performance improvements across various quality metrics. To validate the effectiveness of our method, the fused images should be applied to downstream tasks to assess their contribution. In our work, we utilized IVF image fusion technology for object detection, conducting experiments in traffic and industrial environments. The detection targets included both large and small objects, and the results further demonstrated the strong generalization capability of the MAAM model, achieving state-of-the-art performance across both datasets. To further evaluate the visual performance of our proposed method, we conduct a qualitative comparison with several state-of-the-art fusion-based pedestrian detection methods, including CDDFuse, SWINF, and MMAUNet.