You Only Crash Once v2: Perceptually Consistent Strong Features for One-Stage Domain Adaptive Detection of Space Terrain

TL;DR

This paper introduces YOCOv2, an advanced domain adaptation method for space terrain detection that significantly improves accuracy and efficiency in autonomous spacecraft vision systems, enabling real-time operation with limited data.

Contribution

We develop YOCOv2, an enhanced version of YOCOv1, incorporating novel visual similarity techniques to achieve state-of-the-art unsupervised domain adaptation for space terrain detection.

Findings

Achieves up to 31% performance improvement over YOCOv1.

Outperforms terrestrial state-of-the-art methods in UDA for space terrain.

Demonstrates practical deployment on spacecraft hardware and NASA data.

Abstract

The in-situ detection of planetary, lunar, and small-body surface terrain is crucial for autonomous spacecraft applications, where learning-based computer vision methods are increasingly employed to enable intelligence without prior information or human intervention. However, many of these methods remain computationally expensive for spacecraft processors and prevent real-time operation. Training of such algorithms is additionally complex due to the scarcity of labeled data and reliance on supervised learning approaches. Unsupervised Domain Adaptation (UDA) offers a promising solution by facilitating model training with disparate data sources such as simulations or synthetic scenes, although UDA is difficult to apply to celestial environments where challenging feature spaces are paramount. To alleviate such issues, You Only Crash Once (YOCOv1) has studied the integration of Visual Similarity-based Alignment (VSA) into lightweight one-stage object detection architectures to improve space terrain UDA. Although proven effective, the approach faces notable limitations, including performance degradations in multi-class and high-altitude scenarios. Building upon the foundation of YOCOv1, we propose novel additions to the VSA scheme that enhance terrain detection capabilities under UDA, and our approach is evaluated across both simulated and real-world data. Our second YOCO rendition, YOCOv2, is capable of achieving state-of-the-art UDA performance on surface terrain detection, where we showcase improvements upwards of 31% compared with YOCOv1 and terrestrial state-of-the-art. We demonstrate the practical utility of YOCOv2 with spacecraft flight hardware performance benchmarking and qualitative evaluation of NASA mission data.