Captive balloon image object detection system using deep learning

Abstract

The surveillance of large areas to ensure local security requires remote sensors with high temporal and spatial resolution. Captive balloons with infrared and visible sensors, like ALTAVE captive balloon system, can perform a long-term day–night surveillance and provide security of large areas by monitoring people and vehicles, but it is an exhaustive task for a human. In order to provide a more efficient and less arduous monitoring, a deep learning model was trained to detect people and vehicles in images from captive balloons infrared and visible sensors. Two databases containing about 700 images each, one for each sensor, were manually built. Two networks were fine-tuned from a pretrained faster region-based convolution neural network (R-CNN). The network reached accuracies of 87.1% for the infrared network and 86.1% for the visible one. Both networks were able to satisfactorily detect multiple objects in an image with a variety of angles, positions, types (for vehicles), scales, and even with some noise and overlap. Thus a faster R-CNN pretrained only in common RGB (red, green, and blue) images can be fine-tuned to work satisfactorily on visible remote sensing (RS) images and even on the infrared RS images.