ABSTRACT

Having been developed recently, image classification and object detection by deep convolutional neural networks are now widely used. However, in applications of deep learning in forestry, hardly any cases have involved forestry robots. For the autonomous driving and working of a forwarder on a strip road, a system is developed for detecting strip roads by semantic segmentation using deep learning, and data augmentation methods are proposed on the basis of generative adversarial networks (GANs) to improve robustness. In this study, three GAN-based data augmentation methods are proposed, namely, (i) translated images from new label images, (ii) translated images from an actual dataset, and (iii) both. The training dataset is evaluated by fully convolutional networks, from which the trained models show a pixel accuracy of 0.616 and a mean accuracy of 0.512. Compared with no augmentation and general augmentation, a maximum improvement in accuracy of 0.031 is observed. The GAN-based augmentation technique is effective for detecting a small number class because the class distribution of the dataset is set arbitrarily. Accurate detection by the trained model is confirmed even if the image dataset contains unknown obstacles.

摘要

最近发展起来，通过深度卷积神经网络的图像分类和目标检测现在被广泛使用。但是，在林业深度学习中的应用中，几乎没有任何情况涉及林业机器人。为了在公路上自动驾驶和运行货运代理，开发了一种使用深度学习通过语义分段检测公路的系统，并在生成对抗网络（GAN）的基础上提出了数据增强方法，以提高鲁棒性。在这项研究中，提出了三种基于GAN的数据增强方法，即（i）来自新标签图像的翻译图像，（ii）来自实际数据集的翻译图像，以及（iii）两者。训练数据集通过完全卷积网络进行评估，从中可以得出训练后的模型的像素精度为0。616，平均准确度为0.512。与不进行增强和常规增强相比，可以看到精度最高提高了0.031。基于GAN的扩充技术可有效检测少量类别，因为数据集的类别分布是任意设置的。即使图像数据集包含未知障碍，也可以通过训练有素的模型进行准确检测。