Mobile Image Multi-label Recognition Algorithm Based on PaddlePaddle Platform

Abstract

As one of the core algorithms of machine vision, the mobile image multi-label recognition algorithm has received extensive attention from researchers in recent years and has been widely used in cutting-edge fields such as deep learning framework paddlepaddle platform, video surveillance, intelligent robots, and unmanned aerial vehicles. However, the existing recognition algorithms are not completely satisfied with the practical application in life and production. Due to the complexity of the platform environment, they can often only propose specific solutions based on existing problems, and there is no universal algorithm that is suitable for all kinds of Complex environment. The purpose of this paper is to study the multi-label recognition algorithm of moving images based on PaddlePaddle platform. This research mainly analyzes and researches the mobile image multi-tag space deployment plan and the multi-tag recognition algorithm, and further improves the tag reading rate and recognition reliability of the mobile image on the PaddlePaddle platform. This research first analyzes several key factors that affect the performance of UHF recognition system, considers the improvement plan of PaddlePaddle platform’s mobile image multi-tag recognition algorithm from the two aspects of space diversity and frequency diversity, and finally determines the multiple The label space diversity scheme, and the introduction of a multi-label optimization recognition algorithm to improve the recognition efficiency of the PaddlePaddle platform’s mobile image multi-label. Experimental data shows that the reading rate can reach 0.907 when identifying 300 tags in the experiment, and when the number of tags is greater than 300, the reading rate is close to 1, which verifies that the algorithm proposed in this paper is used in the multi-tag recognition of moving images on the PaddlePaddle platform.