DeepScene, DeepVis, DeepDist, And DeepReject: Image-Based Visibility Estimation System For UAV

This paper presents a method of multiple Deep Learning model-based visibility estimation for a fixed and drone camera under adversarial conditions. High visibility is required for safe flights at airports, cities, and fields. On the other hand, local weather conditions can suddenly vary during the flight. Fog and clouds are known to cause invisibility that impedes critical issues for flights. Due to expensive systems, visibility measurement sensors have been used at very limited airports. For this, lower-cost methods with portable apparatus have been desired for flight routines. Therefore, this paper proposes a camera-based visibility estimation method using multiple Deep Learning models. However, even state-of-the-art models can be degraded by strong illumination like sunbeams and artificial lighting. For this issue, DeepReject is introduced to exclude input images. DeepScene is proposed to apply for panoptic segmentation of objects. In DeepDist, fog levels can be estimated by apparent changes in each geo-tagged object, i.e., vehicle, and mountain. An image-data regression-based DeepVis is also proposed to apply for non-geo-tagged scenes, i.e., sky and clouds. Using four DeepXs, experimental results show the robustness, stability, and accuracy of the proposed multiple DL models for the unmanned aerial vehicle flight conditions over a single DL.