摘 要
随着人工智能技术的快速发展，图像分类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卷积神经网络（Deep Convolutional Neural Network, DCNN）的图像分类算法，以应对复杂场景下高精度分类的需求。传统方法受限于手工特征提取的局限性，难以适应多样化的图像数据，而深度学习模型通过自动学习多层次特征表示，为图像分类提供了新的解决方案。本研究提出了一种改进的深度卷积网络架构，结合残差连接与注意力机制，有效提升了模型在大规模数据集上的泛化能力。此外，针对训练过程中过拟合问题，引入了自适应正则化策略，并设计了一种动态学习率调整方法以加速收敛。实验结果表明，所提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性能，特别是在小样本和噪声数据条件下表现尤为突出。本研究的主要贡献在于提出了一个高效且鲁棒的图像分类框架，不仅增强了模型对复杂特征的学习能力，还为实际应用场景中的部署提供了理论支持，为进一步优化深度学习模型在图像处理领域的应用奠定了基础。
关键词：深度卷积神经网络；图像分类；残差连接；注意力机制；自适应正则化
Abstract
With the rapid development of artificial intelligence technologies, image classification, as one of the core tasks in the field of computer vision, has become a hotspot for both academic research and industrial applications. This study aims to explore image classification algorithms based on deep convolutional neural networks (DCNNs) to address the demand for high-precision classification in complex scenarios. Traditional methods, limited by the constraints of hand-crafted feature extraction, struggle to adapt to diverse image data, whereas deep learning models, through automatic learning of multi-level feature representations, offer new solutions for image classification. In this study, an improved deep convolutional network architecture is proposed, integrating residual connections with attention mechanisms, which effectively enhances the model's generalization capability on large-scale datasets. Furthermore, to tackle overfitting during training, an adaptive regularization strategy is introduced, along with a dynamically adjusted learning rate method designed to accelerate convergence. Experimental results demonstrate that the proposed algorithm achieves significantly better classification performance than existing methods across multiple public benchmark datasets, particularly excelling under conditions of few-shot samples and noisy data. The primary contribution of this study lies in the proposal of an efficient and robust image classification fr amework, which not only strengthens the model's ability to learn complex features but also provides theoretical support for practical deployment in real-world applications, laying a foundation for further optimization of deep learning models in the domain of image processing.
Keywords: Deep Convolutional Neural Network; Image Classification; Residual Connection; Attention Mechanism; Adaptive Regularization
目 录
1绪论 1
1.1研究背景与意义 1
1.2国内外研究现状分析 1
1.3本文研究方法概述 2
2深度卷积网络基础理论 2
2.1卷积神经网络基本原理 2
2.2常见深度卷积网络架构 3
2.3卷积层与池化层的作用 3
2.4激活函数与正则化技术 4
2.5深度学习框架支持 4
3图像分类算法关键技术 5
3.1数据预处理与增强方法 5
3.2特征提取与表示学习 5
3.3分类器设计与优化策略 6
3.4损失函数的选择与改进 6
3.5迁移学习在图像分类中的应用 7
4实验设计与性能评估 7
4.1实验数据集选择与构建 7
4.2模型训练与参数调优 8
4.3性能指标体系设计 8
4.4结果分析与对比研究 9
4.5算法局限性与改进建议 9
结论 10
参考文献 12
致 谢 13
