1 research outputs found
Efficient Semantic Segmentation on Edge Devices
Semantic segmentation works on the computer vision algorithm for assigning
each pixel of an image into a class. The task of semantic segmentation should
be performed with both accuracy and efficiency. Most of the existing deep FCNs
yield to heavy computations and these networks are very power hungry,
unsuitable for real-time applications on portable devices. This project
analyzes current semantic segmentation models to explore the feasibility of
applying these models for emergency response during catastrophic events. We
compare the performance of real-time semantic segmentation models with
non-real-time counterparts constrained by aerial images under oppositional
settings. Furthermore, we train several models on the Flood-Net dataset,
containing UAV images captured after Hurricane Harvey, and benchmark their
execution on special classes such as flooded buildings vs. non-flooded
buildings or flooded roads vs. non-flooded roads. In this project, we developed
a real-time UNet based model and deployed that network on Jetson AGX Xavier
module