Boulders Identification on Small Bodies Under Varying Illumination Conditions
Mattia Pugliatti, Francesco Topputo

TL;DR
This paper presents a data-driven image processing pipeline for robust boulder detection on small bodies, using synthetic environments for training and addressing challenges like illumination variability and irregular shapes.
Contribution
It introduces a multi-step training approach with synthetic data generation in Blender to improve boulder detection under varying illumination conditions.
Findings
High accuracy in synthetic image detection
Good generalization to real images
Effective handling of illumination variability
Abstract
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…
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Taxonomy
TopicsAnomaly Detection Techniques and Applications · Advanced Neural Network Applications · Infrastructure Maintenance and Monitoring
MethodsRoIPool · Softmax · RoIAlign
