Details

Autor(en) / Beteiligte

• Galvez-Serna, Julian
• Mandel, Nicolas
• Sandino, Juan
• Vanegas, Fernando
• Ly, Nam
• Flannery, David Timothy
• Gonzalez, Felipe

Titel

Real-time Segmentation of Desiccation Cracks onboard UAVs for Planetary Exploration

Ist Teil von

• 2022 IEEE Aerospace Conference (AERO), 2022, p.1-12

Ort / Verlag

IEEE

Erscheinungsjahr

2022

Link zum Volltext

Quelle

IEEE Explore

Beschreibungen/Notizen

• Planetary surfaces are a primary focus of space exploration. Some of the most challenging current efforts in planetary exploration relate to the search for life, or biosignatures, in these environments. Detecting water-related textures, and thus evidence for potentially habitable environments, has the potential to focus and accelerate the search for biosignatures on other planets. Desiccation cracks are sedimentary features providing evidence of sediment-water interaction. They are known from both Earth and Mars, and are likely to be found via aerial exploration approaches of ancient lakes, rivers, or shallow marine environments where biosignatures may be found. Current approaches using image processing to detect desiccation cracks focus on segmenting just the cracks and prove somewhat successful. However, the use of Unmanned Aerial Vehicles (UAVs) to detect and highlight areas with desiccation cracks for closer inspection over much larger surface areas has not yet been explored. This paper describes the development and deployment of a desiccation crack detection system using UAVs and AI. We describe data collection at varying heights above ground level and data-augmentation with a range of pixel-level and spatial transforms. Three state-of-the-art CNN segmentation networks are trained and evaluated using PyTorch. The networks are deployed on an edge-AI device integrated with a companion computer onboard a sub-2kg quadrotor UAV. Results indicate that the models can segment desiccation cracks on airborne-collected images at various locations at heights ranging from 5 to 20m. Deployment of the models for real-time inference onboard small UAVs shows potential for application in the field. This research shows the feasibility of a low-volume data training to UAV deployment pipeline while highlighting potential hurdles in the processing pipeline for future efforts. We present a system and architecture for onboard UAV detectors of sedimentary features, which can speed up the search for life on other planets.