Terrestrial Visual Dust Density Estimation Based on Deep Learning

Airborne dust has a broad impact from climate to human health. Extensive dust monitoring can lead to identifying environmental hazards and developing mitigation strategies. However, conventional dust measuring devices are usually expensive and limited for the monitoring of the spatial characteristics of dust. Available RGB camera systems might be a potential tool for the measurement of these spatial characteristics, but the automatic detection of airborne dust within these images is not well-researched. The challenges for the required algorithm for such an automatic detection are manifold, including the opaqueness, the wide range of possible density levels, the visual similarity to effects like smoke or clouds, and the fuzzy boundaries of airborne dust. In order to face these challenges in the underexplored research field of detecting airborne dust in terrestrial RGB images, we propose DeepDust. DeepDust is a dust density estimation neural network and exploits convolutional-based multi-level embeddings to merge features from different resolutions and semantic levels. Due to the absence of existing methods in our research field, we compare results achieved by our DeepDust with techniques from the crowd counting and monocular depth estimation domain on the Meteodata dust dataset. Our DeepDust outperforms the other evaluated approaches regarding regression ability by a wide margin.