A Composite Recognition Method Based on Multimode Mutual Attention Fusion Network

To address the problem of single-mode vulnerability to complex environments, a multimode fusion network with mutual attention is proposed. This network combines the use of laser, infrared and millimeter wave modalities to leverage the advantages of each mode in different environments, increasing the network’s resilience to interference. The study begins with the construction of pixel-level fusion networks, feature-weighted fusion networks and the multimode mutual attention fusion network. A comprehensive introduction to the multimode mutual attention fusion network is given, as well as a comparison with the other two networks. The model is then trained and evaluated using data from glide rocket and drone experiments. Finally, an analysis of the anti-outlier interference capability of the multimode fusion network with mutual attention is carried out. The test results show that the multimode mutual attention fusion network containing a feature fusion attention mechanism has the highest detection performance and anti-interference ability. Without interference, the network achieves a remarkable accuracy of 0.98 for multi-target recognition. In addition, with an accuracy of 0.96, it ensures a high level of stability in various interference environments. In addition, the introduction of multi-scale fusion has improved the rocket’s speed adaptability by about 75%.