Data Augmentation with 3DCG Models for Nuisance Wildlife Detection using a Convolutional Neural Network

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%.