摘 要
随着人工智能技术的快速发展，图像识别作为计算机视觉领域的核心任务之一，已成为学术界和工业界的热点研究方向。本研究旨在探索基于深度学习的图像识别算法，以解决传统方法在复杂场景下性能受限的问题。通过引入卷积神经网络（CNN）及其改进模型，结合迁移学习和数据增强技术，本文提出了一种高效且鲁棒性强的图像识别框架。该框架针对大规模数据集训练过程中存在的过拟合问题，设计了自适应正则化机制，并优化了网络结构以提升特征提取能力。实验结果表明，所提出的算法在多个公开基准数据集上取得了显著优于现有方法的分类精度，特别是在小样本学习和跨领域泛化方面表现出色。此外，本研究还开发了一种轻量级模型，适用于资源受限环境下的实时图像识别任务，为移动设备和嵌入式系统提供了可行的解决方案。总体而言，本研究不仅验证了深度学习在图像识别领域的优越性，还通过技术创新推动了算法的实际应用进程，为未来相关研究奠定了坚实基础。
关键词：深度学习；图像识别；卷积神经网络；迁移学习；小样本学习
Abstract
With the rapid development of artificial intelligence technologies, image recognition, as one of the core tasks in the field of computer vision, has become a hotspot research direction in both academia and industry. This study aims to explore deep learning-based image recognition algorithms to address the performance limitations of traditional methods in complex scenarios. By incorporating convolutional neural networks (CNNs) and their advanced models, along with transfer learning and data augmentation techniques, this paper proposes an efficient and robust image recognition fr amework. To tackle the overfitting issue during the training process on large-scale datasets, an adaptive regularization mechanism is designed, and the network architecture is optimized to enhance feature extraction capabilities. Experimental results demonstrate that the proposed algorithm achieves significantly higher classification accuracy than existing methods on multiple public benchmark datasets, particularly excelling in few-shot learning and cross-domain generalization. Furthermore, this research develops a lightweight model suitable for real-time image recognition tasks in resource-constrained environments, providing a feasible solution for mobile devices and embedded systems. Overall, this study not only verifies the superiority of deep learning in image recognition but also promotes the practical application of algorithms through technological innovation, laying a solid foundation for future related research.
Keywords: Deep Learning；Image Recognition；Convolutional Neural Network；Transfer Learning；Few-Shot Learning
目 录
摘 要 I
Abstract II
一、绪论 1
（一）深度学习与图像识别的研究背景 1
（二）《基于深度学习的图像识别算法研究》的意义 1
（三）国内外研究现状分析 1
二、深度学习基础理论与技术框架 2
（一）深度学习的核心原理概述 2
（二）常用深度学习框架比较分析 2
（三）图像识别中的关键算法介绍 3
（四）深度学习模型优化策略 3
三、图像识别算法设计与实现 4
（一）数据集构建与预处理方法 4
（二）卷积神经网络在图像识别中的应用 4
（三）算法改进方案的设计思路 5
（四）实验环境搭建与参数设置 5
四、性能评估与结果分析 6
（一）评价指标体系的构建 6
（二）不同算法性能对比实验 6
（三）结果分析与问题探讨 7
（四）算法优化方向展望 7
结 论 8
致 谢 9
参考文献 10
