论文标题
部分可观测时空混沌系统的无模型预测
Training Deep Learning Algorithms on Synthetic Forest Images for Tree Detection
论文作者
论文摘要
在森林环境中基于视觉的细分是自主林业操作（例如树木砍伐和转发）的关键功能。深度学习算法显示出令人鼓舞的结果，可以执行视觉任务，例如对象检测。但是，这些算法的监督学习过程需要大量图像的注释。在这项工作中，我们建议使用模拟的森林环境自动生成具有像素级注释的43 K逼真的合成图像，并使用它来训练深度学习算法进行树检测。这使我们能够解决以下问题：i）在苛刻的综合森林环境中，我们应该期望什么样的表现，ii）注释对培训最重要，iii）ii）在RGB和深度之间应使用哪种方式。我们还通过直接预测真实图像上的边界框，分割掩码和关键点来报告在合成数据集上学习的特征的有希望的传输学习能力。在GitHub（https://github.com/norlab-ulaval/perceptreev1）上可用的代码。
Vision-based segmentation in forested environments is a key functionality for autonomous forestry operations such as tree felling and forwarding. Deep learning algorithms demonstrate promising results to perform visual tasks such as object detection. However, the supervised learning process of these algorithms requires annotations from a large diversity of images. In this work, we propose to use simulated forest environments to automatically generate 43 k realistic synthetic images with pixel-level annotations, and use it to train deep learning algorithms for tree detection. This allows us to address the following questions: i) what kind of performance should we expect from deep learning in harsh synthetic forest environments, ii) which annotations are the most important for training, and iii) what modality should be used between RGB and depth. We also report the promising transfer learning capability of features learned on our synthetic dataset by directly predicting bounding box, segmentation masks and keypoints on real images. Code available on GitHub (https://github.com/norlab-ulaval/PercepTreeV1).
