Fast cropping method for proper input size of convolutional neural networks in underwater photography,Journal of the Society for Information Display

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Fast cropping method for proper input size of convolutional neural networks in underwater photography
Journal of the Society for Information Display ( IF 1.7 ) Pub Date : 2020-05-30 , DOI: 10.1002/jsid.911
Jin‐Hyun Park ₁ , Young‐Kiu Choi ₂ , Changgu Kang ₃

Affiliation

The convolutional neural network (CNN) is widely used in object detection and classification and shows promising results. However, CNN has the limitation of fixed input size. If the input image size of the CNN is different from the image size of the system to which the CNN is applied, additional processes, such as cropping, warping, or padding, are necessary. They take additional time to process these processes, and fast cutting methods are required for systems that require real‐time processing. The purpose of our system to which the CNN model will be applied is to classify fish species in real time, using cameras installed in a shallow stream. Therefore, in this paper, we propose a straightforward real‐time image cropping method for fast cutting to the proper input size of CNN. In the experiments, we evaluate the proposed method using CNNs (AlexNet, Vgg 16, Vgg 9, and GoogLeNet).

中文翻译：

水下摄影中卷积神经网络适当输入大小的快速裁剪方法

卷积神经网络（CNN）广泛用于目标检测和分类，并显示出令人鼓舞的结果。但是，CNN具有固定输入大小的限制。如果CNN的输入图像大小与应用CNN的系统的图像大小不同，则需要进行其他处理，例如裁切，扭曲或填充。他们需要花费更多时间来处理这些过程，而对于需要实时处理的系统，则需要快速切割方法。使用CNN模型的系统的目的是使用安装在浅水流中的摄像头对鱼类进行实时分类。因此，在本文中，我们提出了一种直接的实时图像裁剪方法，用于快速裁剪到适当的CNN输入大小。在实验中

更新日期：2020-05-30

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