Research on geometric figure classification algorithm based on Deep Learning

TL;DR

This paper proposes a deep learning-based geometric figure classification algorithm using CNNs, specifically leveraging LeNet-5 architecture to improve recognition accuracy and training efficiency over traditional methods.

Contribution

It introduces a CNN model with shared feature parameters and cross-entropy loss to enhance geometric figure recognition accuracy and training speed.

Findings

Improved recognition accuracy on test datasets.

Faster training process compared to traditional algorithms.

Enhanced generalization of the recognition model.

Abstract

In recent years, with the rapid development of computer information technology, the development of artificial intelligence has been accelerating. The traditional geometry recognition technology is relatively backward and the recognition rate is low. In the face of massive information database, the traditional algorithm model inevitably has the problems of low recognition accuracy and poor performance. Deep learning theory has gradually become a very important part of machine learning. The implementation of convolutional neural network (CNN) reduces the difficulty of graphics generation algorithm. In this paper, using the advantages of lenet-5 architecture sharing weights and feature extraction and classification, the proposed geometric pattern recognition algorithm model is faster in the training data set. By constructing the shared feature parameters of the algorithm model, the cross-entropy loss function is used in the recognition process to improve the generalization of the model and improve the average recognition accuracy of the test data set.