摘要：
随着人工智能技术的快速发展，深度学习作为其核心分支，在图像识别领域取得了显著成就。本文综述了近年来基于深度学习的图像识别技术的最新进展，并探讨了其在多个领域的应用。文章首先介绍了深度学习的基本原理和常见模型，如卷积神经网络（CNN）、循环神经网络（RNN）以及生成对抗网络（GAN）等。随后，详细分析了当前图像识别技术面临的挑战，如数据量不足、过拟合问题、模型泛化能力等，并针对这些问题探讨了相应的解决方案。在应用方面，本文重点讨论了深度学习在医疗影像分析、安全监控、自动驾驶等领域的实际应用案例，并展望了未来的发展趋势。研究表明，深度学习在图像识别领域具有广阔的应用前景，但仍需进一步优化算法、提升模型性能，并加强跨学科合作，以推动其在实际应用中的深入发展。
关键词： 深度学习；图像识别；卷积神经网络；应用研究；最新进展
Abstract:
With the rapid development of artificial intelligence technology, deep learning, as its core branch, has achieved remarkable achievements in the field of image recognition. This paper reviews the latest progress of image recognition technology based on deep learning in recent years and explores its applications in multiple fields. The article first introduces the basic principles and common models of deep learning, such as Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), and Generative Adversarial Networks (GAN). Subsequently, it analyzes the current challenges faced by image recognition technology, such as insufficient data volume, overfitting problems, and model generalization ability, and discusses corresponding solutions to these issues. In terms of applications, this paper focuses on practical application cases of deep learning in medical image analysis, security surveillance, autonomous driving, and other fields, and looks forward to future development trends. Research indicates that deep learning has broad application prospects in the field of image recognition, but further optimization of algorithms, improvement of model performance, and strengthening of interdisciplinary collaboration are still needed to promote its deeper development in practical applications.
Keywords: Deep Learning; Image Recognition; Convolutional Neural Networks; Application Research; Latest Progress
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