Mr. Brett S. Ballard Profile

Brett S. Ballard

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Proceedings Article | 3 October 2023 Poster + Paper

Deep learning approach to vehicle pose estimation from polarimetric image data

Matthew Siefring, Bradley Ratliff, Brett Ballard

Proceedings Volume 12690, 126900M (2023) https://doi.org/10.1117/12.2677123

KEYWORDS: RGB color model, Pose estimation, Polarimetry, Polarization, Light sources and illumination, Network architectures, Data modeling, Feature extraction, Deep learning

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Object pose estimation is an important problem in the field of remote sensing that provides valuable information for target identification tasks. Polarization is a fundamental property of light that contains useful information about the physical properties of an object, such as shape and surface material properties. Polarization imaging has been shown to have advantages over conventional imaging techniques for object detection and feature extraction in a variety of challenging scenarios, including low light, high background clutter, and low visibility conditions. In this work, we investigate using polarimetric imaging to improve the performance of deep learning approaches to object pose estimation on a range of model target vehicles. We collect polarimetric imaging data and labeled ground truth pose data on the target vehicles in a controlled solar simulation laboratory environment under precise sensor, object, and solar source geometries. We first establish baseline performance of our approach by training our network using conventional visible RGB s0 images under favorable lighting conditions. We then make use of the full linear Stokes images for each color channel in various configurations, retrain our network, and compare performance. We furthermore propose an ensemble method to combine features obtained from convolutional neural networks trained on both conventional RGB and Stokes-vector images. These obtained ensemble features are then used to train a multi-layer perceptron. Experimental results demonstrate that combining polarization imaging with conventional imaging can improve feature extraction and the accuracy of deep learning-based approaches to pose estimation.

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