| 深度学习在图像识别中的应用研究综述 |
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| 引用本文： | 郑远攀,李广阳,李晔. 深度学习在图像识别中的应用研究综述[J]. 计算机工程与应用, 2019, 55(12): 20-36. DOI: 10.3778/j.issn.1002-8331.1903-0031 |
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| 作者姓名： | 郑远攀 李广阳 李晔 |
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| 作者单位： | 郑州轻工业大学 计算机与通信工程学院,郑州 450001;应急平台信息技术河南省工程实验室,郑州 450001;郑州轻工业大学 计算机与通信工程学院,郑州,450001 |
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| 基金项目： | 国家自然科学基金;河南省高等学校青年骨干教师资助计划;河南省科技攻关计划;青年骨干教师项目;应急平台信息技术河南省工程实验室开放基金;郑州轻工业大学校内科研基金项目 |
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| 摘 要： | 深度学习作为图像识别领域重要的技术手段,有着广阔的应用前景,开展图像识别技术研究对推动计算机视觉及人工智能的发展具有重要的理论价值和现实意义,文中对深度学习在图像识别中的应用给予综述。介绍了深度学习的由来,具体分析了深度信念网络、卷积神经网络、循环神经网络、生成式对抗网络以及胶囊网络等深度学习模型,对各个深度学习模型的改进型模型逐一对比分析。总结近年来深度学习在人脸识别、医学图像识别、遥感图像分类等图像识别应用领域取得的研究成果并探讨了已有研究值得商榷之处,对深度学习在图像识别领域中的发展趋势进行探讨,指出有效使用迁移学习技术识别小样本数据,使用非监督与半监督学习对图像进行识别,如何对视频图像进行有效识别以及强化模型的理论性等是该领域研究的进一步方向。
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| 关 键 词： | 深度学习 图像识别 卷积神经网络 胶囊网络 迁移学习 非监督学习 |
Survey of Application of Deep Learning in Image Recognition |
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| ZHENG Yuanpan,LI Guangyang,LI Ye. Survey of Application of Deep Learning in Image Recognition[J]. Computer Engineering and Applications, 2019, 55(12): 20-36. DOI: 10.3778/j.issn.1002-8331.1903-0031 |
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| Authors: | ZHENG Yuanpan LI Guangyang LI Ye |
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| Affiliation: | 1.School of Computer and Communication Engineering, Zhengzhou University of Light Industry, Zhengzhou 450001, China2.Henan Engineering Laboratory of Emergency Platform Information Technology, Zhengzhou 450001, China |
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| Abstract: | As an important technical means in the field of image recognition, deep learning has broad application prospects. Carrying out image recognition technology research has important theoretical and practical significance for promoting the development of computer vision and artificial intelligence. The application of deep learning in image recognition gives a review. The origin of deep learning is introduced. Deep learning models such as deep belief network, convolutional neural network, cyclic neural network, generated confrontation network and capsule network are analyzed. The improved models of each deep learning model are compared and analyzed one by one. In this paper, the research results of deep learning in image recognition applications such as face recognition, medical image recognition and remote sensing image classification are summarized. The existing researches are worth discussing. The development trend of deep learning in the field of image recognition is carried out. The discussion points out that the effective use of migration learning technology to identify small sample data, the use of unsupervised learning and semi-supervised learning to identify images, how to effectively identify video images and the theoretical significance of the model are further directions in this field. |
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| Keywords: | deep learning image recognition convolutional neural network capsule network transfer learning unsupervised learning |
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