Synthetic augmentation methods for object detection in infrared overhead imagery

Abstract

Geospatial intelligence is a subject with many opportunities for machine automation. Object detection is one desirable application. However, a lack of high-volume relevant datasets can make this task difficult. To combat this issue, we introduced a spin-set augmentation technique to generate synthetic training data. We used these synthetic datasets to augment the training of an object detection deep network, focusing on visible band imagery. We have continued our efforts by further testing this method on long-wave infrared imagery, including results from YOLO, SSD, and Faster R-CNN algorithms. We also introduce another synthetic augmentation technique which involves generating physics-based fully-rendered images of 3D synthetic scenery and targets and compared the rendered image performance to that of spin-sets. This paper analyzes both the spin-set and rendered image augmentation techniques in terms of object detection performance, complexity, generalizability, and explainability.