# Multimodal Network for Object Detection Using Channel Adjustment and Multi-Scale Attention
###### Abstract  
Object detection benefits greatly from multimodal image fusion, which integrates complementary data from different modalities like RGB and thermal images. However, existing methods struggle with effective inter-modal fusion, particularly in capturing spatial and contextual information across diverse regions and scales. To address these limitations, we propose the dynamic channel adjustment and multi-scale activated attention mechanism network (MNCM). Our approach incorporates dynamic channel adjustment for precise feature fusion across modalities and a multi-scale attention mechanism to capture both local and global contexts. This design improves robustness while balancing computational efficiency. The model's scalability is enhanced through its ability to adaptively process multi-scale information without being constrained by fixed-scale designs. To validate our method, we used two multimodal datasets from traffic and industrial scenarios, which consisted of paired thermal infrared and visible light images. The results first demonstrate strong performance in multimodal fusion and then show state-of-the-art results in object detection, proving its effectiveness for real-world applications. Therefore, we propose a novel multimodal image fusion network, a Multimodal Network for Object Detection Using Channel Adjustment and Multi-Scale Attention in the backbone branches for each modality. This design achieves both inter-modality complementarity and intra-modality enhancement during the fusion process. The dynamic channel adjustment (DCA) module dynamically adjusts channels to optimize fusion and enhance modality complementarity, allowing the network to adaptively focus on the most relevant features from each modality. Meanwhile, the multi-scale activated attention mechanism (MAAM) strengthens attention to key features across multiple receptive field scales, ensuring that both local and global contexts are effectively captured. In contrast to existing transformer-based multimodal fusion models, which predominantly rely on global token-level attention for feature integration, our approach emphasizes fine-grained channel-level interaction and scale-aware attention modulation. This design reduces computational overhead while maintaining strong performance, particularly in scenarios with diverse object sizes and modality variations. Together, these components enable flexible and efficient inter-modality feature interactions, significantly improving the quality of fused images and overall detection performance. By leveraging these advanced mechanisms, our network is able to achieve superior adaptability to varying object sizes and complex environments, making it a robust solution for real-world detection tasks. We conducted extensive experiments on traffic and industrial datasets to thoroughly evaluate the performance of the module in multimodal fusion. Initially, we assessed the image fusion performance, which demonstrated the strong capability of the two modules in integrating multimodal information. Subsequently, the results from object detection experiments showed that our approach significantly improved detection performance in complex scenarios. Notably, it exhibited exceptional adaptability and robustness, particularly in handling objects of varying sizes and challenging environments. These findings underscore the model's strong generalization ability, which offer a promising solution for multimodal image fusion in real-world applications. Furthermore, extensive computational efficiency analysis demonstrates that our model achieves this superior performance while maintaining competitive inference speed and parameter efficiency, making it suitable for real-time applications. Our main contributions are summarized as follows:  
* To achieve effective feature interaction and adaptive channel selection, we design a dynamic channel adjustment strategy using channel exchange principles for enhancing the fusion of complementary information from different modalities.