摘 要
随着人工智能技术的快速发展，图像识别已成为计算机视觉领域的研究热点之一，其在医疗诊断、自动驾驶、安防监控等实际场景中具有重要应用价值。本研究旨在探索基于深度学习的图像识别技术，通过构建高效且精确的模型以解决传统方法在复杂场景下性能不足的问题。研究采用卷积神经网络（CNN）作为核心框架，并结合迁移学习和数据增强技术优化模型性能，同时引入注意力机制提升关键特征的提取能力。实验部分选取了多个公开数据集进行验证，包括ImageNet、CIFAR-10以及自建行业数据集，结果表明所提出的方法在分类准确率和计算效率方面均优于现有主流算法。此外，本研究还设计了一种轻量化网络结构，适用于资源受限的边缘计算环境，进一步拓展了技术的应用范围。
关键词：深度学习 图像识别 卷积神经网络
Abstract
With the rapid development of artificial intelligence technology, image recognition has become one of the research hotspots in the field of computer vision, and it has important application value in medical diagnosis and diagnosis, autonomous driving, security monitoring and other practical scenarios. This study aims to explore deep learning-based image recognition techniques by constructing efficient and accurate models to address the underperformance of conventional methods in complex scenarios. The study uses convolutional neural network (CNN) as the core fr amework, and combines transfer learning and data enhancement techniques to optimize the model performance, and introduces attention mechanism to improve the extraction ability of key features. In the experimental section, several publicly available datasets were selected for validation, including ImageNet, CIFAR-10 and self-built industry data set. The results show that the proposed method is better than the existing mainstream algorithms in terms of classification accuracy and computational efficiency. Moreover, this study also designed a lightweight network structure suitable for resource-constrained edge computing environments, which further expanded the application scope of the technology.
Keyword:Deep Learning Image Recognition Convolutional Neural Network
目 录
引言 1
1深度学习基础理论研究 1
1.1深度学习基本概念 1
1.2神经网络结构分析 2
1.3卷积神经网络原理 2
1.4深度学习优化算法 3
2图像识别关键技术分析 3
2.1图像预处理方法 3
2.2特征提取技术研究 4
2.3数据增强技术应用 4
2.4分类器设计与优化 5
3深度学习模型在图像识别中的应用 5
3.1常用深度学习模型概述 5
3.2模型选择与性能评估 5
3.3转移学习在图像识别中的作用 6
3.4实时图像识别系统设计 6
4图像识别技术的挑战与改进策略 7
4.1小样本学习问题探讨 7
4.2鲁棒性提升方法研究 7
4.3多模态数据融合技术 8
4.4可解释性与透明性分析 8
结论 8
参考文献 10
致谢 11
