摘 要
随着人工智能技术的快速发展，基于深度学习的图像识别技术已成为计算机视觉领域的研究热点，其在模式识别、智能监控、医疗诊断等多领域展现出巨大潜力本研究以提升图像识别性能为目标，深入探讨了深度学习模型在图像特征提取与分类中的应用价值通过构建改进型卷积神经网络（CNN）架构并引入迁移学习策略，有效解决了传统方法在复杂场景下识别精度低及数据依赖性强的问题实验结果表明，所提出的模型在多个公开数据集上取得了优于现有主流算法的表现，特别是在小样本和噪声干扰条件下仍能保持较高的鲁棒性此外，本研究还将该技术应用于实际场景，如交通标志识别和医学影像分析，验证了其在真实环境中的可行性和有效性总体而言，本研究的主要贡献在于提出了一种高效且灵活的深度学习框架，不仅显著提升了图像识别的准确率，还为相关领域的进一步发展提供了新的思路和参考
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, deep learning-based image recognition has become a research hotspot in the field of computer vision, demonstrating significant potential in various domains such as pattern recognition, intelligent surveillance, and medical diagnosis. This study aims to enhance the performance of image recognition by thoroughly investigating the application value of deep learning models in feature extraction and classification. By constructing an improved convolutional neural network (CNN) architecture and incorporating transfer learning strategies, this research effectively addresses the limitations of traditional methods, including low accuracy in complex scenarios and strong data dependency. Experimental results indicate that the proposed model outperforms existing mainstream algorithms on multiple public datasets, particularly exhibiting high robustness under conditions of small sample sizes and noise interference. Furthermore, this study applies the developed technology to practical scenarios, such as traffic sign recognition and medical image analysis, thereby validating its feasibility and effectiveness in real-world environments. Overall, the primary contribution of this research lies in the proposal of an efficient and flexible deep learning fr amework, which not only significantly improves the accuracy of image recognition but also provides new insights and references for further advancements in related fields.
Keywords: Deep Learning; Image Recognition; Convolutional Neural Network; Transfer Learning; Few-Shot Learning
目 录
摘 要 I
ABSTRACT II
引言 1
第1章 深度学习与图像识别基础 2
1.1 深度学习基本原理 2
1.2 图像识别技术概述 2
1.3 深度学习在图像识别中的应用现状 3
1.4 关键算法与模型介绍 3
1.5 技术发展驱动因素分析 4
第2章 数据集构建与预处理方法 5
2.1 图像数据集的选取与构建 5
2.2 数据增强技术研究 5
2.3 数据标注方法及其挑战 6
2.4 预处理算法优化策略 6
2.5 数据质量对模型性能的影响 6
第3章 深度学习模型设计与优化 8
3.1 卷积神经网络结构设计 8
3.2 模型训练方法研究 8
3.3 超参数调优策略分析 9
3.4 模型压缩与加速技术 9
3.5 可解释性与鲁棒性提升方法 9
第4章 图像识别技术的实际应用探索 11
4.1 医疗影像识别应用研究 11
4.2 自动驾驶中的图像识别技术 11
4.3 安防监控领域应用案例 12
4.4 移动设备上的实时图像识别 12
4.5 未来应用场景展望 12
结论 14
参考文献 15
致 谢 16
