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首页> 外文会议>Biennial International Conference on Engineering, Science, Construction, and Operations in Challenging Environments >Identification of Targeted Regions on an Analogue Site of the Moon by Using Deep Learning Segmentation Algorithm
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Identification of Targeted Regions on an Analogue Site of the Moon by Using Deep Learning Segmentation Algorithm

机译：利用深度学习分割算法识别月球模拟场地上的目标区域

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In this study, a possibility in use and performance of a deep-learning segmentation algorithm are investigated for automatically identifying targeted local regions, which could be hardly separated by a clear feature line. The 4 classes targeted in this study are topographically related objects or regions such as small crater, slope of mound, rock, and icy area in different colors, etc. Even if it is not exactly the same as the ones on the Moon in terms of its surrounding environment, their shape and colors even in different scales were tried to mimic as likely as possible. In order to acquire the information, the shape of the object in the image must be accurately segmented individually. The mask region-based convolutional neural network (Mask R-CNN), an open-source deep learning instance segmentation algorithm, has been adopted in this study for that purpose. A labeled data set is composed from the rover images, which has been taken on the analogue lunar. Then, a number of training are undertaken in a set of conditions and verified in a standard manner. It is shown that object identification and regional segmentation are highly workable with good agreement in comparison of the known and predicted regional information. This kind of deep learning application could be further enhanced for providing a key information to support a lunar surface exploration in future.
机译：在这项研究中，我们研究了一种深度学习分割算法的使用可能性和性能，用于自动识别难以被清晰的特征线分割的目标局部区域。本研究针对的4个类别是与地形相关的物体或区域，如小陨石坑、土丘斜坡、岩石和不同颜色的冰区等。即使在周围环境方面与月球上的物体或区域不完全相同，也尽可能模仿它们的形状和颜色，即使在不同的尺度下。为了获取信息，必须对图像中物体的形状进行精确的单独分割。基于掩模区域的卷积神经网络（mask R-CNN）是一种开源的深度学习实例分割算法。标记的数据集由月球车图像组成，这些图像是在模拟月球车上拍摄的。然后，在一系列条件下进行大量培训，并以标准方式进行验证。结果表明，与已知和预测的区域信息相比，目标识别和区域分割具有很好的可操作性和一致性。这种深度学习应用可以进一步增强，为未来月球表面探测提供关键信息。

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