Research on Self-service Customs Clearance System at Border Crossings Based on Deep Learning Models

Abstract

This paper proposes a deep learning method for face recognition in the self-service customs clearance system at border crossings and designs the encoder and face feature mining module in the learning framework. Meanwhile, the loss function is constructed by combining L1 loss and KL scatter. The face recognition technology based on the deep learning model is used to construct the self-service border crossing system, and the research and analysis are conducted from two aspects, namely, the test of the self-service border crossing system and the application situation. The number of outbound self-clearance acceptors has increased by 2957931, and the self-clearance system at border crossings is able to provide more travelers with the convenience brought by self-clearance. This study solves the problem of self-clearance at border crossing with the help of face recognition technology in a deep learning model, which provides technical support and theoretical reference for the optimization and upgrading of self-clearance system at border crossing in the future.