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。本研究聚焦于基于深度学习的图像识别算法，旨在解决传统方法难以应对的复杂场景下图像识别精度低、泛化能力差等问题。通过引入卷积神经网络（CNN）、循环神经网络（RNN）等先进模型结构，并结合迁移学习、数据增强等优化策略，构建了一套高效稳定的图像识别系统。实验结果表明，在多个公开数据集上，该算法相较于传统方法及部分现有深度学习模型，准确率提升了约15%，召回率提高了近10%。特别是在处理模糊、遮挡等特殊情况下，表现出显著优势。本研究创新性地提出了一种自适应特征融合机制，能够根据不同任务需求动态调整特征权重，有效提升了模型的鲁棒性和适应性。此外，还针对实际应用场景中的计算资源限制问题，设计了轻量化网络架构，实现了在保证识别性能的同时降低运算成本。该研究成果不仅为图像识别领域提供了新的思路和技术手段，也为相关应用如智能安防、医疗影像诊断等奠定了坚实基础。
关键词：深度学习 图像识别 卷积神经网络
Abstract
With the rapid development of information technology, image recognition technology has played an indispensable role in numerous fields. This study focuses on deep learning-based image recognition algorithms, aiming to address the low accuracy and poor generalization capabilities of traditional methods in complex scenarios. By incorporating advanced model architectures such as Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN), along with optimization strategies like transfer learning and data augmentation, an efficient and stable image recognition system has been developed. Experimental results demonstrate that, compared to traditional methods and some existing deep learning models, the proposed algorithm achieves approximately a 15% increase in accuracy and nearly a 10% improvement in recall rate across multiple public datasets. Notably, it exhibits significant advantages in handling special cases such as blurred and occluded images. This research innovatively proposes an adaptive feature fusion mechanism that dynamically adjusts feature weights according to different task requirements, effectively enhancing the robustness and adaptability of the model. Furthermore, addressing the issue of computational resource limitations in practical application scenarios, a lightweight network architecture has been designed to reduce computational costs while maintaining recognition performance. This research not only provides new ideas and technical approaches for the field of image recognition but also lays a solid foundation for related applications such as intelligent security and medical image diagnosis.
Keyword:Deep Learning Image Recognition Convolutional Neural Network
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 1
1.3研究方法与技术路线 1
2深度学习基础理论 2
2.1深度学习基本概念 2
2.2常用深度学习模型 3
2.3图像识别中的深度学习框架 3
3图像识别算法研究 4
3.1卷积神经网络优化 4
3.2特征提取与表达 4
3.3数据增强与预处理 5
4应用案例分析 6
4.1医疗影像识别应用 6
4.2自动驾驶视觉系统 6
4.3智能安防监控应用 7
结论 7
参考文献 9
致谢 10
