Data Augmentation with 3DCG Models for Nuisance Wildlife Detection using a Convolutional Neural Network
Data Augmentation with 3DCG Models for Nuisance Wildlife Detection using a Convolutional Neural Network
复制标题
DOI：
10.12792/icisip2021.032
复制
发表时间：
2021
期刊：
影响因子：
--
通讯作者：
Ryoke Naoya;H. Kitakaze;Ryo Matsumura
中科院分区：
文献类型：
--
作者：
Ryoke Naoya;H. Kitakaze;Ryo Matsumura
在本文中，我们提出了一种数据增强方法，使用3DCG模型滋扰野生动物检测。营养野生动物对农作物的破坏已成为农民的一个主要问题，导致他们的积极性下降。因此，迫切需要采取措施防止野生动物的破坏。为此，我们正在使用卷积神经网络（CNN）开发一种讨厌的野生动物驱避系统。因此，有必要收集令人讨厌的野生动物的训练图像。这是一个非常困难的任务，但我们提出的方法可以很容易地解决它。我们获得的实验结果表明，CNN可以使用我们的方法生成的图像进行训练，我们训练的模型的准确率为92%。
In this paper, we propose a data augmentation method using 3DCG models for nuisance wildlife detection. Nuisance wildlife damage to crops has become a major problem for farmers, leading to a decline in their motivation. There-fore, there is an urgent need for countermeasures against wildlife damage. To that end, we are developing a nuisance wildlife repellent system using a convolutional neural network (CNN). Therefore, it is necessary to collect training images of nuisance wildlife. This is a very difficult task, but the method we propose can solve it easily. We obtain experimental results that show that a CNN can be trained using the images generated by our method, and our trained model has an accuracy level of 92%.