| 卢宏涛,罗沐昆.基于深度学习的计算机视觉研究新进展[J].数据采集与处理,2022,37(2):247-278 |
| 基于深度学习的计算机视觉研究新进展 |
| Survey on New Progresses of Deep Learning Based Computer Vision |
| 投稿时间：2022-02-10 修订日期：2022-03-01 |
| DOI：10.16337/j.1004-9037.2022.02.001 |
| 中文关键词: 深度学习 目标检测 图像分割 超分辨率 计算机视觉 |
| 英文关键词:deep learning object detection image segmentation super-resolution computer vision |
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| 摘要点击次数: 961 |
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| 中文摘要: |
| 近年来，深度学习在计算机视觉各个领域中的应用成效显著，新的深度学习方法和深度神经网络模型不断涌现，算法性能被不断刷新。本文着眼于2016年以来的一些典型网络和模型，对基于深度学习的计算机视觉研究新进展进行综述。首先总结了针对图像分类的主流深度神经网络模型，包括标准模型及轻量化模型等；然后总结了针对不同计算机视觉领域的主流方法和模型，包括目标检测、图像分割和图像超分辨率等；最后总结了深度神经网络搜索方法。 |
| 英文摘要: |
| Deep learning has recently achieved great breakthroughs in some fields of computer vision. Various new deep learning methods and deep neural network models were proposed， and their performance was constantly updated. This paper makes a survey on the new progresses of applications of deep learning on computer vision since 2016 with emphases on some typical networks and models. We first investigate the mainstream deep neural network models for image classification including standard models and light-weight models. Then， we introduce some main methods and models for different computer vision fields including object detection， image segmentation and image super-resolution. Finally， we summarize deep neural network architecture searching methods. |
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