XD-STOD: Cross-Domain Superresolution for Tiny Object Detection

Monitoring the restoration of natural habitats after human intervention is an important task in the field of remote sensing. Currently, this requires extensive field studies entailing considerable costs. Unmanned Aerial vehicles (UAVs, a.k.a. drones) have the potential to reduce these costs, but generate immense amounts of data which have to be evaluated automatically with special techniques. Especially the automated detection of tree seedlings poses a big challenge, as their size and shape vary greatly across images. In addition, there is a tradeoff between different flying altitudes. Given the same camera equipment, a lower flying altitude achieves higher resolution images and thus, achieving high detection rates is easier. However, the imagery will only cover a limited area. On the other hand, flying at larger altitudes, allows for covering larger areas, but makes seedling detection more challenging due to the coarser images. In this paper we investigate the usability of super resolution (SR) networks for the case that we can collect a large amount of coarse imagery on higher flying altitudes, but only a small amount of high resolution images from lower flying altitudes. We use a collection of high-resolution images taken by a drone at 5m altitude. After training the SR models on these data, we evaluate their applicability to low quality images taken at 30m altitude (in-domain). In addition, we investigate and compare whether approaches trained on a highly diverse large data sets can be transferred to these data (cross-domain). We also evaluate the usability of the SR results based on their influence on the detection rate of different object detectors. We found that the features acquired from training on standard SR data sets are transferable to the drone footage. Furthermore, we demonstrate that the detection rate of common object detectors can be improved by SR techniques using both settings, in-domain and cross-domain.