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。本研究旨在探索深度学习模型在图像识别中的应用潜力，并提出一种改进的卷积神经网络架构以提升识别精度与效率。研究首先分析了传统图像识别方法存在的局限性，如特征提取能力不足和对复杂场景适应性较差等问题，进而引入深度学习技术作为解决方案。通过构建多层卷积神经网络并结合数据增强、迁移学习等策略，本研究实现了对大规模图像数据集的有效训练与优化。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方法的分类准确率，特别是在小样本和噪声干扰条件下表现尤为突出。此外，研究还设计了一种轻量级网络结构，显著降低了计算资源消耗，为实际应用场景提供了可行性支持。综上所述，本研究不仅验证了深度学习在图像识别领域的优越性能，还通过技术创新推动了该领域向更高效、更智能的方向发展，为未来相关研究奠定了重要基础。
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 technologies, image recognition based on deep learning has become a research hotspot in the field of computer vision. This study aims to explore the application potential of deep learning models in image recognition and proposes an improved convolutional neural network architecture to enhance both accuracy and efficiency. The research first analyzes the limitations of traditional image recognition methods, such as insufficient feature extraction capabilities and poor adaptability to complex scenes, and subsequently introduces deep learning as a solution. By constructing multi-layer convolutional neural networks and integrating strategies such as data augmentation and transfer learning, this study achieves effective training and optimization of large-scale image datasets. Experimental results demonstrate that the proposed model outperforms existing methods in classification accuracy across multiple public datasets, particularly excelling under conditions of few samples and noisy interference. Additionally, the study designs a lightweight network structure that significantly reduces computational resource consumption, providing feasible support for practical application scenarios. In summary, this research not only verifies the superior performance of deep learning in image recognition but also promotes the advancement of the field toward greater efficiency and intelligence through technological innovation, laying an important foundation for future related studies.
Keyword:Deep Learning Image Recognition Convolutional Neural Network
目 录
引言 1
1深度学习与图像识别基础 1
1.1深度学习基本原理 1
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背景 2
2主流深度学习模型分析 3
2.1卷积神经网络结构特点 3
2.2循环神经网络的应用场景 3
2.3变体模型的性能比较 4
3图像识别关键技术研究 4
3.1数据预处理方法探讨 4
3.2特征提取与优化策略 5
3.3分类算法的改进与实现 5
4实验设计与结果分析 5
4.1实验环境与数据集选择 6
4.2性能评估指标体系 6
4.3实验结果分析与讨论 6
结论 7
参考文献 8
致谢 9
