Stefanuk Braden, Skonieczny Krzysztof
Aerospace Robotics Laboratory, Electrical and Computer Engineering, Concordia University, Montreal, QC, Canada.
Front Robot AI. 2022 Oct 14;9:974397. doi: 10.3389/frobt.2022.974397. eCollection 2022.
In the domain of planetary science, novelty detection is gaining attention because of the operational opportunities it offers, including annotated data products and downlink prioritization. Using a variational autoencoder (VAE), this work improves upon state-of-the-art novelty detection performance in the context of Martian exploration by (measured by the area under the receiver operating characteristic curve (ROC AUC)). Autoencoders, especially VAEs, perform well across all classes of novelties defined for Martian exploration. VAEs are shown to have high recall in the Martian context, making them particularly useful for on-ground processing. Convolutional autoencoders (CAEs), on the other hand, demonstrate high precision making them good candidates for onboard downlink prioritization. In our implementation adversarial autoencoders (AAEs) are also shown to perform on par with state-of-the-art. Dimensionality reduction is a key feature of autoencoders for novelty detection. In this study the impact of dimensionality reduction on detection quality is explored, showing that both VAEs and AAEs achieve comparable ROC AUCs to CAEs despite observably poorer (blurred) image reconstructions; this is observed both in Martian data and in lunar analogue data.
在行星科学领域，新奇性检测正受到关注，因为它提供了包括带注释的数据产品和下行链路优先级排序在内的操作机会。通过使用变分自编码器（VAE），这项工作在火星探测背景下将最先进的新奇性检测性能提高了（通过接收者操作特征曲线下面积（ROC AUC）来衡量）。自编码器，尤其是VAE，在为火星探测定义的所有新奇性类别中表现良好。结果表明，VAE在火星探测背景下具有较高的召回率，这使得它们在地面处理中特别有用。另一方面，卷积自编码器（CAE）显示出高精度，使其成为机载下行链路优先级排序的理想选择。在我们的实现中，对抗自编码器（AAE）也表现得与最先进技术相当。降维是用于新奇性检测的自编码器的一个关键特性。在本研究中，探讨了降维对检测质量的影响，结果表明，尽管VAE和AAE的图像重建明显较差（模糊），但它们在火星数据和月球模拟数据中均实现了与CAE相当的ROC AUC。
