Differential morphological profile neural network for maneuverability hazard detection in unmanned aerial system imagery

J. Alex Hurt; Grant J. Scott; David Huangal; Jeffrey Dale; Trevor M. Bajkowski; James M. Keller; Stanton R. Price

doi:10.1117/12.2585843

12 April 2021 Differential morphological profile neural network for maneuverability hazard detection in unmanned aerial system imagery

J. Alex Hurt, Grant J. Scott, David Huangal, Jeffrey Dale, Trevor M. Bajkowski, James M. Keller, Stanton R. Price

Author Affiliations +

Proceedings Volume 11746, Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications III; 1174629 (2021) https://doi.org/10.1117/12.2585843
Event: SPIE Defense + Commercial Sensing, 2021, Online Only

Abstract

Within computer vision, deep neural networks (DNNs) have gained tremendous popularity in recent years due to their ability to extract and classify visual features. As this technology has become more widespread, some of the shortcomings of the DNNs have become apparent. Recently, researchers have found that DNNs prefer to learn texture from visual signals rather than shape and have observed that more shape extraction correlates to better DNN performance. DNN have been applied to a vast number of problems, with excellent results in many of them, including object detection. The combination of DNN feature extractors with regression and region proposal techniques, like the Faster Region-Proposal Convolutional Neural Network (Faster R-CNN) and Single Shot Detector (SSD), have yielded promising results. Meanwhile, from the field of digital image processing, grayscale morphology extracts shape information using morphological operations. The Differential Morphological Profile (DMP) performs morphological opening and closings with varied structuring element sizes and computes the absolute difference between the resulting steps. The DMP provides a mechanism to improve shape extraction within DNN, and increase model robustness. To that end, a DMP-based neural network, DMPNet, has been created to assist DNN with extracting shape information by adding layers that perform DMP prior to the first convolutional layer. We use the DMPNet as a feature extractor for Faster R-CNN and SSD, and apply it to Maneuverability Hazard Detection in unmanned aerial system (UAS) imagery. The benefits of this approach include better explainability, lower training times, and models more tuned to shape information.

Conference Presentation

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J. Alex Hurt, Grant J. Scott, David Huangal, Jeffrey Dale, Trevor M. Bajkowski, James M. Keller, and Stanton R. Price "Differential morphological profile neural network for maneuverability hazard detection in unmanned aerial system imagery", Proc. SPIE 11746, Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications III, 1174629 (12 April 2021); https://doi.org/10.1117/12.2585843

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