Affiliation:
1. Guangxi Arts University, Nanning 530007, China
Abstract
Unlike traditional image recognition technology, DL can automatically extract features and improve recognition accuracy by combining feature extraction and classification. The challenges and shortcomings of traditional image recognition methods are discussed in this article, as well as the development process and research status of DL. Related theories in image recognition based on deep learning (DL) are proposed, DL’s basic models and methods are analyzed, and related image data sets are demonstrated experimentally. Furthermore, because DL is typically used for large sample sets, this paper proposes an improved algorithm based on small samples, as well as a DNN-based analysis model for the evolution of ancient large figurine images. This model, when compared to the traditional neural network model, can speed up the network’s convergence speed and reduce training time to a certain extent. This model improves the rate of image recognition while lowering the error rate.
Funder
2020 Start-Up Fund Project for Scientific Research of High-Level Talents
Subject
Electrical and Electronic Engineering,Computer Networks and Communications,Information Systems
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