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Data Augmentation for Plant Classification

机译：用于植物分类的数据扩充

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摘要

Data augmentation plays a crucial role in increasing the number of training images, which often aids to improve classification performances of deep learning techniques for computer vision problems. In this paper, we employ the deep learning framework and determine the effects of several data-augmentation (DA) techniques for plant classification problems. For this, we use two convolutional neural network (CNN) architectures, AlexNet and GoogleNet trained from scratch or using pre-trained weights. These CNN models are then trained and tested on both original and data-augmented image datasets for three plant classification problems: Folio, AgrilPlant, and the Swedish leaf dataset. We evaluate the utility of six individual DA techniques (rotation, blur, contrast, scaling, illumination, and projective transformation) and several combinations of these techniques, resulting in a total of 12 data-augmentation methods. The results show that the CNN methods with particular data-augmented datasets yield the highest accuracies, which also surpass previous results on the three datasets. Furthermore, the CNN models trained from scratch profit a lot from data augmentation, whereas the fine-tuned CNN models do not really profit from data augmentation. Finally, we observed that data-augmentation using combinations of rotation and different illuminations or different contrasts helped most for getting high performances with the scratch CNN models.
机译：数据增强在增加训练图像的数量方面起着至关重要的作用，这通常有助于改善深度学习技术对计算机视觉问题的分类性能。在本文中，我们采用了深度学习框架，并确定了几种数据增强（DA）技术对植物分类问题的影响。为此，我们使用了两种卷积神经网络（CNN）架构，即AlexNet和GoogleNet从头开始或使用预先训练的权重进行训练。然后针对三个植物分类问题在原始图像和数据增强的图像数据集上对这些CNN模型进行训练和测试，以解决三个植物分类问题：Folio，AgrilPlant和瑞典叶数据集。我们评估了六种单独的DA技术（旋转，模糊，对比度，缩放，照明和投影变换）的实用性以及这些技术的几种组合，总共产生了12种数据增强方法。结果表明，具有特定数据增强数据集的CNN方法具有最高的准确性，也超过了三个数据集上的先前结果。此外，从头开始训练的CNN模型从数据增强中获得了很多好处，而经过微调的CNN模型并没有从数据增强中真正受益。最后，我们观察到使用旋转和不同照明或不同对比度的组合进行的数据增强最有助于使用草稿CNN模型获得高性能。

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