论文标题
使用全趋化神经网络对小行星上的自主火山口检测
Autonomous crater detection on asteroids using a fully-convolutional neural network
论文作者
论文摘要
本文显示了使用CERES U-NET（一个完全跨局部神经网络）使用自主火山口检测。根据LRO和手动火山口目录收集的数据，对U-NET进行了对月球全球形态镶嵌的光学图像的训练。月球训练的网络将在CERES的黎明光学图像上进行测试：此任务是通过转移学习（TL）方法来完成的。训练有素的模型已使用100、500和1000张CERES进行微调。测试性能是在从未见过的350张图像上测量的，分别达到96.24％，96.95％和97.19％的测试精度。这意味着，尽管月球和谷神星之间存在固有的差异，但TL仍能令人鼓舞。 U-NET的输出包含预测的陨石坑：将其后处理，应用全局阈值进行图像二进制化以及匹配算法的模板以在像素空间中提取陨石坑位置和RADII。将对后处理的陨石坑进行计数并将其与地面真相数据进行比较，以计算图像分割指标：精度，召回和F1分数。将计算这些指数，并将讨论它们的效果，例如自动化火山口编目和光学导航。
This paper shows the application of autonomous Crater Detection using the U-Net, a Fully-Convolutional Neural Network, on Ceres. The U-Net is trained on optical images of the Moon Global Morphology Mosaic based on data collected by the LRO and manual crater catalogues. The Moon-trained network will be tested on Dawn optical images of Ceres: this task is accomplished by means of a Transfer Learning (TL) approach. The trained model has been fine-tuned using 100, 500 and 1000 additional images of Ceres. The test performance was measured on 350 never before seen images, reaching a testing accuracy of 96.24%, 96.95% and 97.19%, respectively. This means that despite the intrinsic differences between the Moon and Ceres, TL works with encouraging results. The output of the U-Net contains predicted craters: it will be post-processed applying global thresholding for image binarization and a template matching algorithm to extract craters positions and radii in the pixel space. Post-processed craters will be counted and compared to the ground truth data in order to compute image segmentation metrics: precision, recall and F1 score. These indices will be computed, and their effect will be discussed for tasks such as automated crater cataloguing and optical navigation.
