Deep Learning-based Real-time Segmentation for Edge Computing Devices

Jaeho Kwak, Hyunwoo Yu, Yubin Cho, Suk-Ju Kang, Jaechan Cho, Jun-Young Park, Ji-Won Lee

Abstract: Recently, due to the rapid improvement of artificial intelligence technology, numerous studies are considered to solve various problems using deep learning. Typical deep neural networks for semantic segmentation require the high computation with a large capacity to extract abundant amounts of contextual information for accurate prediction. Our live demonstration will show real-time semantic segmentation operation on an NVIDIA Jetson-Xavier board with the BiSeNet-based method compressed using a novel knowledge distillation method.