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、自动化检测和智能系统中的应用日益广泛本研究旨在探讨深度学习算法在图像识别中的性能优化及其实际应用价值通过分析卷积神经网络（CNN）的核心原理及其改进模型，提出了一种融合多尺度特征提取与注意力机制的新型网络架构该方法有效提升了复杂场景下目标检测的准确率和鲁棒性实验结果表明，相较于传统CNN模型，所提方法在公开数据集上的分类精度提高了约3.5%，同时推理速度提升近20%此外，研究还将该技术应用于医学影像分析和自动驾驶领域，验证了其在真实场景中的可行性与优越性研究表明，基于深度学习的图像识别技术不仅能够显著改善识别效果，还为智能化社会的发展提供了重要技术支持。
关键词：深度学习 图像识别 卷积神经网络 多尺度特征提取
Abstract
With the rapid development of artificial intelligence technology, Image recognition technology based on deep learning has become a research hotspot in the field of computer vision, Its application in pattern recognition, automatic detection and intelligent system. This study aims to explore the performance optimization and practical application value of deep learning algorithms in image recognition. By analyzing the core principle of convolutional neural network (CNN) and its improvement model, The method effectively improves the accuracy and robustness of target detection in complex scenarios and shows that, Compared to the traditional CNN model, The classification accuracy of the proposed method on publicly available datasets is improved by about 3.5%, Meanwhile, the reasoning speed has increased by nearly 20%. In addition, The research has also applied the technology to medical imaging analysis and autonomous driving, To verify its feasibility and superiority in real scenarios shows that, Deep learning-based image recognition technology can not only significantly improve the recognition effect, It also provides important technical support for the development of intelligent society.
Keyword:Deep Learning Image Recognition Convolutional Neural Network Multi-scale Feature Extraction
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深度学习基础理论与技术框架 2
2.1深度学习基本原理 2
2.2常用深度学习模型综述 2
2.3图像识别中的深度学习算法 3
2.4深度学习框架在图像识别中的应用 3
3图像识别关键技术研究 4
3.1数据预处理与增强技术 4
3.2特征提取与表示方法 4
3.3卷积神经网络优化策略 5
3.4多模态图像识别技术分析 5
4深度学习在图像识别中的应用实践 6
4.1医疗影像识别案例研究 6
4.2自动驾驶场景中的图像识别 6
4.3面部识别技术的实际应用 7
4.4工业检测中的图像识别解决方案 7
结论 8
参考文献 9
致谢 10
