深度卷积神经网络在计算机视觉中的应用研究综述

随着大数据时代的到来，含更多 隐含层的深度卷积神经网络(Convolutional neural networks, CNNs)具有更复杂的网络结构，与传统机器学习方法相比具有更强大的特征学习和特征表达能力。使用深度学习算法训练的卷积神经网络模型自提出以来在计算机视觉领域的多个大规模识别任务上取得了令人瞩目的 成绩。本文首先简要介绍深度学习和卷积神经网络的兴起与展，概述卷积神经网络的基本模型结构、卷积特征提取和池化操作。然后综述了基于深度学习的卷积神经网络模型在图像分类、物体检测、姿态估计、图像分割和人脸识别等多个计算机视觉应用领域中的研究现状 和发展趋势，主要从典型的网络结构的构建、训练方法和性能表现3个方面进行介绍。最后对目前研究中存在的一些问题进行简要的总结和讨论，并展望未来发展的新方向。

Applications of Deep Convolutional Neural Network in Computer Vision

Deep learning has recently achieved breakthrough progress in speech recognition and image recognition. With the advent of big data era, deep convolutional neural networks with more hidden layers and more complexarchitectures have more powerful ability of feature learning and feature representation. Convolutional neural network models trained by deep learning algorithm have attained remarkable performance in many large scale recognition tasks of computer vision since they are presented. In this paper, the arising and development of deep learning and convolutional neural network are briefly introduced, with emphasis on the basic structure of convolutional neural network as well as feature extraction using convolution and pooling operations. The current research status and trend of convolutional neural networks based on deep learning and their applications in computer vision are reviewed, such as image classification, object detection, pose estimation, image segmentation and face detection etc. Some related works are introduced from the following three aspects, i.e., construction of typical network structures, training methods and performance. Finally, some existing problems in the present research are briefly summarized and discussed and some possible new directions for future development are prospected.