# Fusion Classification Method Based on Audiovisual Information Processing
## 5 Conclusions  
Traditional monomodal recognition systems, due to their inherent limitations in data representation and sensitivity to environmental conditions, are increasingly unable to meet the robustness requirements for target recognition. Faced with significant technical challenges, such as the difficulty in aligning heterogeneous modality features and the substantial semantic gap across modalities, this study proposes a multimodal fusion framework based on audio-visual fusion. The network architecture employs a 2D CNN and a 1D CNN to extract semantic features from images and sounds, respectively, thereby providing high-quality modality-specific feature representations for subsequent fusion.  
To address the heterogeneity issues in modality fusion and to solve the problem of heterogeneity among modes, a feature fusion strategy based on self-attention mechanism is proposed in this paper. Comparison with the latest multimodal fusion recognition methods: Compared with the PCTDF method, the proposed method increases the average recognition rate by 2.6%. Compared with the STNet method, the recognition accuracy of the proposed method is increased by 2.4%. Compared with the AFT-SAM method, the proposed method increases the average recognition rate by 1.2%. Compared with the PSFMMA method, the recognition accuracy of the proposed method is increased by 0.9%. These results demonstrate that the proposed M-AVFA algorithm has significant advantages in multimodal fusion, effectively integrating audio and visual information to enhance target recognition performance in complex environments.  
The current multimodal fusion-based target classification in this study is primarily proposed based on convolutional neural networks (CNNs). Future research will attempt to explore deeper network architectures, such as ResNet structures. Future work will prioritize validating the proposed methods through classification experiments in complex outdoor environments. Additionally, further modifications to the current audio-visual fusion approach to achieve target classification under extreme weather conditions remain an area for future investigation.  
P.C. contributed to this paper. Conceptualization, X.Z. and H.Z.; methodology, P.C. and H.C.; writing--original draft preparation, P.C., X.C. and X.L. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the Innovative Research Group Project of the National Natural Science Foundation of China (51821003), the Excellent Youth foundation of Shanxi Province (202103021222011), the Key Research and Development Project of Shanxi Province (202202020101002), the Fundamental Research Program of Shanxi Province (202303021211150), the Aviation Science Foundation (2022Z0220U0002), and the Shanxi Province Key Laboratory of Quantum Sensing and Precision Measurement (201905D121001).  
The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding authors.  
Thanks to all the authors for their help with this paper. Here I would like to thank [PERSON] for putting forward the corresponding concept of this paper, and providing analysis, experimental verification and data preservation; Thanks to [PERSON] for his support of the concepts and methods proposed in this paper, and for his help in the software writing and visualization; Thanks to [PERSON] for the situation analysis of this paper, the investigation of the relevant background of the paper, and the help in the experimental verification and data preservation;Thanks to [PERSON] and [PERSON] for their supervision, theoretical guidance, project and fund support for the first draft of this paper.  
The authors declare no conflicts of interest.