Leveraging synthetic imagery to train deep learning algorithms for the detection of objects of interest in radiant energy imagery

Megan E. Gonzalez; Amanda Catlett; Joseph E. Jabour; Reena R. Patel; Stanton R. Price

doi:10.1117/12.2585770

12 April 2021 Leveraging synthetic imagery to train deep learning algorithms for the detection of objects of interest in radiant energy imagery

Megan E. Gonzalez, Amanda Catlett, Joseph E. Jabour, Reena R. Patel, Stanton R. Price

Author Affiliations +

Proceedings Volume 11728, Algorithms for Synthetic Aperture Radar Imagery XXVIII; 1172803 (2021) https://doi.org/10.1117/12.2585770
Event: SPIE Defense + Commercial Sensing, 2021, Online Only

Abstract

Radiant energy object detection deep learning algorithms require large training sets with site-specific images, often from locations that are difficult to access, while also remaining diverse enough to encourage a robust model. Of particular interest is the detection of buried and partially buried objects which have a widely varying behavior profile dependent on factors such as depth, soil composition, time of day, moisture level, target composition, etc. The variety associated with these variables increases the difficulty of acquiring an adequately diverse data set. Synthetic imagery offers a potential solution to limited accessibility to data as images can be created on demand with diversity limited only by the parameters of the simulation. The goal of this study is to create custom models using SSD (Single Shot MultiBox Detector), YOLOv3 (You Only Look Once), and Faster R-CNN (Region- based Convolutional Neural Networks) to detect buried objects in real images by leveraging synthetic radiant energy imagery. Custom training is done on a synthetic data set (made in-house) using pre-trained models from Tensor ow's model zoo and ImageAI's YOLOv3 pre-trained model. Model training leverages high performance computing (HPC) resources and utilizes GPU to optimize training speed. Proof-of-concept models for SSD, YOLOv3, and Faster R-CNN have been trained on preliminary synthetic imagery and analyzed. Preliminary results for these models will be discussed.

Conference Presentation

Citation Download Citation

Megan E. Gonzalez, Amanda Catlett, Joseph E. Jabour, Reena R. Patel, and Stanton R. Price "Leveraging synthetic imagery to train deep learning algorithms for the detection of objects of interest in radiant energy imagery", Proc. SPIE 11728, Algorithms for Synthetic Aperture Radar Imagery XXVIII, 1172803 (12 April 2021); https://doi.org/10.1117/12.2585770

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