计算机与数字工程2024，Vol.52Issue(10)：2855-2862,8.DOI:10.3969/j.issn.1672-9722.2024.10.001
基于改进矩阵胶囊神经网络交通标志识别算法
Traffic Sign Recognition Algorithm Based on Improved Matrix Capsule Neural Network
摘要
Abstract
Traffic sign recognition is one of the most important functions of driving assistant system.However,the motion blur in the traffic sign images has great impact on the traffic sign recognition accuracy.A traffic sign recognition algorithm based on im-proved matrix capsule neural network is proposed to solve this problem.Matrix capsule neural network is used to mix the capsules in lower capsule layer and generate ones in higher layer.It can build the correlations among features and generate higher level features.Traffic sign category could be inferred with the help of activations of classification capsules.The encoder used to generate the feature in primary capsule will be pre-trained by siamese neural network.This process can make the feature code of traffic signs more dis-criminative.The pre-trained encoder can be used to generate the feature of primary capsule.The experiment show that with the pro-posed method the convergence difficulty of matrix capsule neural network can be relieved and improve the motion-blurred traffic sign recognition accuracy by building feature correlation.关键词
机器视觉/孪生神经网络/胶囊神经网络/交通标志Key words
machine vision/siamese neural network/capsule neural network/traffic sign分类
信息技术与安全科学引用本文复制引用
吕秉略,奚峥皓,邵宇超..基于改进矩阵胶囊神经网络交通标志识别算法[J].计算机与数字工程,2024,52(10):2855-2862,8.基金项目
国家自然科学基金项目"面向广义宽基线立体像对的目标三维重建技术研究"(编号:61801286)资助. （编号:61801286）
