Han Zhou
ABSTRACT
This paper designs a multi-branch feature fusion classification algorithm to improve the network accuracy of the classical deep learning-based infrared image algorithms. First, the algorithm uses a multi-resolution sub-network parallel connection method to build the overall network architecture. Then, a lightweight structural module is designed to reduce the computational load of network weight parameters, and a channel attention module is introduced to refine feature channels and improve detection accuracy. Finally, the parallel connection mode of the spatial pyramid is designed to enhance the ability of feature semantic expression. The experimental results show the improved accuracy of the algorithm model proposed in this paper and the optimization of parameters. The accuracy rate can reach 97.6%. The proposed algorithm is an innovation to the current mainstream classification algorithm, which reflects good promotion and application.
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