摘 要
随着信息技术的迅猛发展，图像识别技术在众多领域发挥着不可替代的作用，但现有图像识别算法存在准确率有待提高、对复杂环境适应性差等问题。为此，本研究聚焦于基于深度学习的图像识别算法优化，旨在提升图像识别的准确性与鲁棒性。研究采用卷积神经网络作为基础架构，引入注意力机制以增强模型对关键特征的关注度，并设计了多尺度特征融合模块来获取更丰富的图像信息。同时，提出一种自适应数据增强方法，通过模拟多样化的实际场景，扩充训练样本集，使模型能够更好地应对不同条件下的图像识别任务。实验结果表明，所提出的优化算法在多个公开数据集上均取得了优于传统方法的性能表现，特别是在复杂背景和低分辨率图像识别方面优势明显。该研究不仅为图像识别领域提供了新的思路和技术手段，而且其创新性的算法改进对于推动深度学习在计算机视觉方向的应用具有重要意义。
关键词：图像识别 深度学习 卷积神经网络 注意力机制
Abstract
With the rapid development of information technology, image recognition technology plays an irreplaceable role in numerous fields; however, existing image recognition algorithms suffer from issues such as insufficient accuracy and poor adaptability to complex environments. To address these challenges, this study focuses on optimizing deep learning-based image recognition algorithms to enhance both accuracy and robustness. By adopting convolutional neural networks as the fundamental architecture, this research introduces an attention mechanism to increase the model's focus on critical features and designs a multi-scale feature fusion module to capture richer image information. Additionally, an adaptive data augmentation method is proposed, which simulates diverse real-world scenarios to expand the training dataset, thereby enabling the model to better handle image recognition tasks under varying conditions. Experimental results demonstrate that the proposed optimized algorithm outperforms traditional methods on multiple public datasets, particularly in complex background and low-resolution image recognition. This research not only provides new perspectives and technical approaches for the field of image recognition but also contributes significantly to advancing the application of deep learning in computer vision through its innovative algorithmic improvements.
Keyword: Image recognition Deep learning Convolutional neural network Attention mechanism
目 录
1 引言 1
2 深度学习算法基础理论 1
2.1 深度学习基本原理 1
2.2 常用深度学习框架 2
2.3 图像识别中的经典模型 2
2.4 模型优化的基本概念 3
3 算法优化策略研究 3
3.1 网络结构优化设计 3
3.2 损失函数改进方法 4
3.3 优化算法选择与应用 5
3.4 数据增强技术分析 5
4 实验验证与结果分析 6
4.1 实验环境与数据集 6
4.2 性能评估指标体系 6
4.3 实验结果对比分析 7
5 结论 8
参考文献 9
致谢 10
