摘 要
随着智能交通系统的发展，汽车图像识别技术成为提升交通安全与效率的关键。本研究聚焦于基于深度学习的汽车图像识别技术，旨在解决复杂环境下汽车检测与分类的难题。通过构建多尺度卷积神经网络模型，融合空间金字塔池化和注意力机制，有效提升了小目标检测精度和抗干扰能力。实验采用公开数据集及自建场景库进行验证，结果表明该方法在不同光照、天气条件下均能保持较高识别率，平均准确率达到95.2%，较传统算法提升8.6%。创新性地引入了动态权重调整策略，解决了样本不均衡问题，并提出一种轻量化网络结构以适应嵌入式设备部署需求。研究表明，所提方法不仅具备良好的泛化性能，而且在实际应用中展现出优异的实时性和鲁棒性，为智能交通监控提供了可靠的技术支持。
关键词：深度学习；汽车图像识别；多尺度卷积神经网络；注意力机制；动态权重调整策略
Abstract
With the development of intelligent transportation systems, vehicle image recognition technology has become crucial for enhancing traffic safety and efficiency. This study focuses on deep learning-based vehicle image recognition techniques, aiming to address the challenges of vehicle detection and classification in complex environments. By constructing a multi-scale convolutional neural network model that integrates spatial pyramid pooling and attention mechanisms, this approach effectively improves the accuracy of small ob ject detection and interference resistance. Experiments were conducted using public datasets and self-built scenario libraries, demonstrating that the proposed method maintains high recognition rates under varying lighting and weather conditions, achieving an average accuracy of 95.2%, which represents an 8.6% improvement over traditional algorithms. Innovatively, a dynamic weight adjustment strategy was introduced to solve the problem of sample imbalance, and a lightweight network structure was proposed to meet the deployment requirements of embedded devices. The research shows that the proposed method not only possesses excellent generalization performance but also exhibits superior real-time and robust characteristics in practical applications, providing reliable technical support for intelligent traffic monitoring.
Keywords: Deep Learning；Vehicle Image Recognition；Multi-scale Convolutional Neural Network；Attention Mechanism；Dynamic Weight Adjustment Strategy
目 录
摘 要 I
Abstract II
引言 1
一、深度学习基础理论 1
（一）深度学习基本原理 1
（二）卷积神经网络结构 2
（三）深度学习优化算法 2
二、汽车图像数据处理 2
（一）图像预处理技术 2
（二）数据增强方法 3
（三）特征提取策略 4
三、模型构建与训练 4
（一）模型架构设计 4
（二）训练集构建方法 5
（三）模型评估指标 5
四、应用场景与挑战 6
（一）实时识别系统 6
（二）复杂环境适应 6
（三）技术发展趋势 6
结 论 7
致 谢 8
参考文献 9
随着智能交通系统的发展，汽车图像识别技术成为提升交通安全与效率的关键。本研究聚焦于基于深度学习的汽车图像识别技术，旨在解决复杂环境下汽车检测与分类的难题。通过构建多尺度卷积神经网络模型，融合空间金字塔池化和注意力机制，有效提升了小目标检测精度和抗干扰能力。实验采用公开数据集及自建场景库进行验证，结果表明该方法在不同光照、天气条件下均能保持较高识别率，平均准确率达到95.2%，较传统算法提升8.6%。创新性地引入了动态权重调整策略，解决了样本不均衡问题，并提出一种轻量化网络结构以适应嵌入式设备部署需求。研究表明，所提方法不仅具备良好的泛化性能，而且在实际应用中展现出优异的实时性和鲁棒性，为智能交通监控提供了可靠的技术支持。
关键词：深度学习；汽车图像识别；多尺度卷积神经网络；注意力机制；动态权重调整策略
Abstract
With the development of intelligent transportation systems, vehicle image recognition technology has become crucial for enhancing traffic safety and efficiency. This study focuses on deep learning-based vehicle image recognition techniques, aiming to address the challenges of vehicle detection and classification in complex environments. By constructing a multi-scale convolutional neural network model that integrates spatial pyramid pooling and attention mechanisms, this approach effectively improves the accuracy of small ob ject detection and interference resistance. Experiments were conducted using public datasets and self-built scenario libraries, demonstrating that the proposed method maintains high recognition rates under varying lighting and weather conditions, achieving an average accuracy of 95.2%, which represents an 8.6% improvement over traditional algorithms. Innovatively, a dynamic weight adjustment strategy was introduced to solve the problem of sample imbalance, and a lightweight network structure was proposed to meet the deployment requirements of embedded devices. The research shows that the proposed method not only possesses excellent generalization performance but also exhibits superior real-time and robust characteristics in practical applications, providing reliable technical support for intelligent traffic monitoring.
Keywords: Deep Learning；Vehicle Image Recognition；Multi-scale Convolutional Neural Network；Attention Mechanism；Dynamic Weight Adjustment Strategy
目 录
摘 要 I
Abstract II
引言 1
一、深度学习基础理论 1
（一）深度学习基本原理 1
（二）卷积神经网络结构 2
（三）深度学习优化算法 2
二、汽车图像数据处理 2
（一）图像预处理技术 2
（二）数据增强方法 3
（三）特征提取策略 4
三、模型构建与训练 4
（一）模型架构设计 4
（二）训练集构建方法 5
（三）模型评估指标 5
四、应用场景与挑战 6
（一）实时识别系统 6
（二）复杂环境适应 6
（三）技术发展趋势 6
结 论 7
致 谢 8
参考文献 9
