Detecting Water in Visual Image Streams from UAV with Flight Constraints

Abstract

Unmanned Ariel Vehicles (UAVs) require identifying water surfaces during flight maneuvers, mainly for safety in execution and its applications. We introduce two novel techniques to identify water surfaces from front-facing and downward-facing cameras mounted on a UAV. The first method — UNet-RAU, a unique architecture based on UNet and Reflection Attention Units, segments water pixels from front-facing camera views, utilizing the reflection property of water surfaces. On the On-Road and Off-Road datasets of Puddle-1000, UNet-RAU improved its performance by 2% over the state-of-the-art FCN-RAU. Additionally, the UNet-RAU generated an F1-score of 80.97% on our Drone-Water-Front dataset. The second method — Dense Optical Flow based Water Detection (DOF-WD), detects water surfaces in videos of downward-facing cameras. This method utilizes downwash-generated ripples and natural texture features on a water surface to identify water in low and high altitudes, respectively. We empirically validated the performance of the DOF-WD method using our Drone-Water-Down dataset.