Real-Time Multi-Modal Image Matching Based on Lightweight Learning Model

This paper proposes an efficient algorithm for real-time multi-modal image matching based on a lightweight feature fusion network, targeting the challenges of multi-modal image matching in multi-source data analysis. The algorithm addresses significant multi-modal feature differences and real-time processing limitations by incorporating key technologies including reparameterization in convolutional neural networks, multi-scale image pyramids, and feature fusion modules. The matching process employs a coarse-to-fine strategy, ensuring robust performance in complex environments. Experimental results using multi-modal datasets demonstrate that the proposed algorithm achieves superior accuracy and speed, with a success rate of 98.3% and an average matching time of 30.51 ms per 500×500 image pair. These results highlight the practical value and strong generalization capability of the algorithm in real-time applications.