论文标题
在不同的照明条件下，在小物体上识别巨石
Boulders Identification on Small Bodies Under Varying Illumination Conditions
论文作者
论文摘要
检测小体表面上巨石的能力对基于视觉的应用（例如在关键操作过程中的导航和危害检测）有益。由于各种不规则形状，巨石种群的特征以及照明条件的快速变异性，此任务是具有挑战性的。作者通过设计一种多步训练方法来应对这一挑战，以开发数据驱动的图像处理管道，以稳健地检测和细分巨石散布在小物体的表面上。由于标记的图像面罩对的可用性有限，因此开发的方法由专门针对这项工作设计的两个人工环境支持。这些用于生成大量的合成图像标签集，这些集合可公开用于图像处理社区。介绍的方法解决了各种照明条件，不规则形状，快速训练时间，对建筑设计空间的广泛探索以及从以前飞行的任务中的合成图像和真实图像之间的域间隙。在合成图像和真实图像上测试了开发的图像处理管道的性能，表现出良好的表现和高概括能力
The capability to detect boulders on the surface of small bodies is beneficial for vision-based applications such as navigation and hazard detection during critical operations. This task is challenging due to the wide assortment of irregular shapes, the characteristics of the boulders population, and the rapid variability in the illumination conditions. The authors address this challenge by designing a multi-step training approach to develop a data-driven image processing pipeline to robustly detect and segment boulders scattered over the surface of a small body. Due to the limited availability of labeled image-mask pairs, the developed methodology is supported by two artificial environments designed in Blender specifically for this work. These are used to generate a large amount of synthetic image-label sets, which are made publicly available to the image processing community. The methodology presented addresses the challenges of varying illumination conditions, irregular shapes, fast training time, extensive exploration of the architecture design space, and domain gap between synthetic and real images from previously flown missions. The performance of the developed image processing pipeline is tested both on synthetic and real images, exhibiting good performances, and high generalization capabilities
