Operational data augmentation in classifying single aerial images of animals

Emmanuel Okafor, Rik Smit, Lambert Schomaker, M. Wiering
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引用次数: 30

Abstract

In deep learning, data augmentation is important to increase the amount of training images to obtain higher classification accuracies. Most data-augmentation methods adopt the use of the following techniques: cropping, mirroring, color casting, scaling and rotation for creating additional training images. In this paper, we propose a novel data-augmentation method that transforms an image into a new image containing multiple rotated copies of the original image in the operational classification stage. The proposed method creates a grid of n×n cells, in which each cell contains a different randomly rotated image and introduces a natural background in the newly created image. This algorithm is used for creating new training and testing images, and enhances the amount of information in an image. For the experiments, we created a novel dataset with aerial images of cows and natural scene backgrounds using an unmanned aerial vehicle, resulting in a binary classification problem. To classify the images, we used a convolutional neural network (CNN) architecture and compared two loss functions (Hinge loss and cross-entropy loss). Additionally, we compare the CNN to classical feature-based techniques combined with a k-nearest neighbor classifier or a support vector machine. The results show that the pre-trained CNN with our proposed data-augmentation technique yields significantly higher accuracies than all other approaches.
航拍动物单幅图像分类的操作数据增强
在深度学习中,数据增强对于增加训练图像的数量以获得更高的分类精度非常重要。大多数数据增强方法采用以下技术:裁剪、镜像、色彩投射、缩放和旋转来创建额外的训练图像。在本文中,我们提出了一种新的数据增强方法,在操作分类阶段将图像转换为包含原始图像的多个旋转副本的新图像。该方法创建了一个由n×n单元格组成的网格,其中每个单元格包含不同的随机旋转图像,并在新创建的图像中引入自然背景。该算法用于创建新的训练和测试图像,增强了图像中的信息量。在实验中,我们使用无人机创建了一个新的数据集,其中包含奶牛和自然场景背景的航拍图像,从而产生了一个二元分类问题。为了对图像进行分类,我们使用了卷积神经网络(CNN)架构,并比较了两种损失函数(Hinge损失和交叉熵损失)。此外,我们将CNN与经典的基于特征的技术(结合k近邻分类器或支持向量机)进行比较。结果表明,使用我们提出的数据增强技术进行预训练的CNN的准确率明显高于所有其他方法。
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