期刊
OPTICAL ENGINEERING
卷 58, 期 4, 页码 -出版社
SPIE-SOC PHOTO-OPTICAL INSTRUMENTATION ENGINEERS
DOI: 10.1117/1.OE.58.4.040901
关键词
visual recognition; image classification; convolutional neural network; deep convolutional neural network; network optimization
类别
In recent years, convolutional neural networks (CNNs) have been widely used in various computer visual recognition tasks and have achieved good results compared with traditional methods. Image classification is one of the basic and important tasks of visual recognition, and the CNN architecture applied to other visual recognition tasks (such as object detection, object localization, and semantic segmentation) is generally derived from the network architecture in image classification. We first summarize the development history of CNNs and then analyze the architecture of various deep CNNs in image classification. Furthermore, not only the innovation of the network architecture is beneficial to the results of image classification, but also the improvement of the network optimization method or training method has improved the result of image classification. We also analyze each of these methods' effect. The experimental results of various methods are compared. Finally, the development of future CNNs is prospected. (C) 2019 Society of Photo-Optical Instrumentation Engineers (SPIE).
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