基于深度学习的交通标志识别系统设计与实现
摘要：本论文设计和实现了一个基于深度学习的交通标志识别系统。交通标志在道路交通管理中起着重要作用，因此开发一个精确、高效的交通标志识别系统具有重要意义。本文采用了卷积神经网络（CNN）作为深度学习模型，并通过对交通标志图像数据集的准备与预处理、模型选择与设计、数据集划分与训练等步骤进行系统设计和实现。在系统实现过程中，我们搭建了相应的系统框架，实现了数据集的加载与预处理模块、深度学习模型的实现模块、模型的训练与优化模块以及模型的测试与验证模块。通过测试和验证，本系统取得了显著的识别准确率和效率，证明了基于深度学习的交通标志识别系统在实际应用中的潜力和重要性。本研究为交通标志识别领域的研究和实践提供了有价值的参考。
关键词：深度学习，交通标志识别，卷积神经网络，数据集处理，模型训练与优化
abstract:
In this paper, a traffic sign identification system is designed and implemented. Traffic signs plays an important role in road traffic management, so it is of important significance to develop an accurate and efficient traffic sign identification system. In this paper, convolutional neural network (CNN) is adopted as the deep learning model, and the system is designed and implemented through the preparation and preprocessing of traffic sign image data set, model selection and design, data set division and training. In the process of system implementation, we set up the corresponding system fr amework, and realized the loading and preprocessing module of data set, the implementation module of deep learning model, the training and optimization module of model, and the test and verification module of model. Through testing and verification, the system achieves significant identification accuracy and efficiency, and proves the potential and importance of a deep learning-based traffic sign identification system in practical applications. This study provides a valuable reference for the research and practice in the field of traffic sign identification.
Key words: deep learning, traffic sign recognition, convolutional neural network, data set processing, model training and optimization
目录
基于深度学习的交通标志识别系统设计与实现 1
一、绪论 4
1.1 研究背景和意义 4
1.2 国内外研究现状 4
二、深度学习与图像识别简介 4
2.1 深度学习概述 5
2.2 图像识别技术综述 5
2.3 卷积神经网络介绍 5
三、系统设计 6
3.1 系统需求分析 6
3.2 数据集准备与预处理 6
3.3 模型选择与设计思路 7
3.4 数据集划分与训练过程 8
四、系统实现 8
4.1 系统框架搭建 8
4.2 数据集加载与预处理模块 9
4.3 深度学习模型的实现 9
4.4 模型训练与优化 10
4.5 模型测试与验证 10
五、结论 11
摘要：本论文设计和实现了一个基于深度学习的交通标志识别系统。交通标志在道路交通管理中起着重要作用，因此开发一个精确、高效的交通标志识别系统具有重要意义。本文采用了卷积神经网络（CNN）作为深度学习模型，并通过对交通标志图像数据集的准备与预处理、模型选择与设计、数据集划分与训练等步骤进行系统设计和实现。在系统实现过程中，我们搭建了相应的系统框架，实现了数据集的加载与预处理模块、深度学习模型的实现模块、模型的训练与优化模块以及模型的测试与验证模块。通过测试和验证，本系统取得了显著的识别准确率和效率，证明了基于深度学习的交通标志识别系统在实际应用中的潜力和重要性。本研究为交通标志识别领域的研究和实践提供了有价值的参考。
关键词：深度学习，交通标志识别，卷积神经网络，数据集处理，模型训练与优化
abstract:
In this paper, a traffic sign identification system is designed and implemented. Traffic signs plays an important role in road traffic management, so it is of important significance to develop an accurate and efficient traffic sign identification system. In this paper, convolutional neural network (CNN) is adopted as the deep learning model, and the system is designed and implemented through the preparation and preprocessing of traffic sign image data set, model selection and design, data set division and training. In the process of system implementation, we set up the corresponding system fr amework, and realized the loading and preprocessing module of data set, the implementation module of deep learning model, the training and optimization module of model, and the test and verification module of model. Through testing and verification, the system achieves significant identification accuracy and efficiency, and proves the potential and importance of a deep learning-based traffic sign identification system in practical applications. This study provides a valuable reference for the research and practice in the field of traffic sign identification.
Key words: deep learning, traffic sign recognition, convolutional neural network, data set processing, model training and optimization
目录
基于深度学习的交通标志识别系统设计与实现 1
一、绪论 4
1.1 研究背景和意义 4
1.2 国内外研究现状 4
二、深度学习与图像识别简介 4
2.1 深度学习概述 5
2.2 图像识别技术综述 5
2.3 卷积神经网络介绍 5
三、系统设计 6
3.1 系统需求分析 6
3.2 数据集准备与预处理 6
3.3 模型选择与设计思路 7
3.4 数据集划分与训练过程 8
四、系统实现 8
4.1 系统框架搭建 8
4.2 数据集加载与预处理模块 9
4.3 深度学习模型的实现 9
4.4 模型训练与优化 10
4.5 模型测试与验证 10
五、结论 11
