Assessing Monocular Depth Estimation Networks for UAS Deployment in Rainforest Environments

The primary objective of this study was to utilize state-of-the-art deep learning-based monocular depth estimation models to assist UAS pilots in rainforest canopy data collection and navigation. Monocular depth estimation models provide a complementary technique to other depth measurement and estimation techniques to extend the range and improve measurements. Several state-of-the-art models were evaluated using a novel dataset composed of data from a simulated rainforest environment. In the evaluation, MiDaS outperformed the other models, and a segmentation pipeline was designed using this model to identify the highest areas of the canopies. The segmentation pipeline was evaluated using 1080p and 360p input videos from the simulated rainforest dataset. It was able to achieve an IoU of 0.848 and 0.826 and an F1 score of 0.915 and 0.902 at each resolution, respectively. We incorporated the proposed depth-estimation-based segmentation pipeline into an example application and deployed it on an edge system. Experimental results display the capabilities of a UAS using the segmentation pipeline for rainforest data collection.