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.
CITATION STYLE
Samaranayake, H., Mudannayake, O., Perera, D., Kumarasinghe, P., Suduwella, C., De Zoysa, K., & Wimalaratne, P. (2023). Detecting Water in Visual Image Streams from UAV with Flight Constraints. Journal of Visual Communication and Image Representation, 96. https://doi.org/10.1016/j.jvcir.2023.103933
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