基于深度学习图像识别技术研究综述
李爽
【摘要】深度学习是目前的机器学习研究中非常重要的一部分，它通过模仿人类大脑的机制来处理数据，靠训练计算机完成自主学习、判断和决策等人类的行为，来构建模拟人脑进行分析学习的神经网络。深度学习在图像识别技术研究上的深入，更是对计算机视觉的推动和人工智能的发展有着极其重要的理论价值和现实意义。本文介绍了基于深度学习的图像识别算法(包括R-CNN,SPP-Net, Fast R-CNN ,faster R-CNN,YOLO and SDD)并且讨论了近年来深度学习在人脸识别、医学图像识别、车牌识别方面的研究成果，最后是对图像识别技术研究提出问题及展望。
【关键词】深度学习；图像识别；R-CNN, SPP-Net ; Fast R-CNN ;faster R-CNN;YOLO ; SDD
Research review of image recognintion technology based on deep learning
LI Shuang
【Abstract 】Deep learning is a very important part of current machine learning research. It processes data by mimicking the mechanisms of the human brain. A neural network simulating human brain for analysis and learning is constructed by training the computer to complete autonomous learning, judgment, decision-making and other human behaviors. The in-depth learning in the research of image recognition technology is of great theoretical value and practical significance to the promotion of computer vision and the development of artificial intelligence. This page introduces several deep learning network models which was commonly used in image processing (such as R-CNN,SPP-Net, Fast R-CNN ,faster R-CNN,YOLO and SDD) and discusses the research results of deep learning in face recognition, medical image recognition, license plate recognition in recent years. Finally, the paper puts forward the problems and prospects of image recognition technology.
【Keywords】deep learning ;image recognition ； R-CNN,SPP-Net; Fast R-CNN ;faster R-CNN;YOLO ;SDD
1 引言
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