Detecting functional objects using multi-modal data

Seth T. Ellis; Andre V. Harrison

doi:10.1117/12.2664059

12 June 2023 Detecting functional objects using multi-modal data

Seth T. Ellis, Andre V. Harrison

Proceedings Volume 12538, Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications V; 125381N (2023) https://doi.org/10.1117/12.2664059
Event: SPIE Defense + Commercial Sensing, 2023, Orlando, Florida, United States

Conference Poster

Abstract

The output of a semantic segmentation model on an off-road dataset can provide an accurate description of the terrain and the obstacles contained within it. This output can be leveraged to determine the presence of barriers in an image. An obstacle is anything that may obstruct a portion of the region of traversal, while we define a barrier as something that will bisect the region of traversal to create two disjoint regions that would otherwise be connected if not for its presence. Detecting instances of barriers requires more than learning the correct label for a standard 2D semantic segmentation model. This paper will present an approach to detect the presence of barriers/barricades in the scene by utilizing the traversability of the semantic classes of non-traversal and the pose of that class(es) in relation to other classes of non-traversal in the scene to define an object as a barricade/barrier.

Citation Download Citation

Seth T. Ellis and Andre V. Harrison "Detecting functional objects using multi-modal data", Proc. SPIE 12538, Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications V, 125381N (12 June 2023); https://doi.org/10.1117/12.2664059

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