MAVNet: An effective semantic segmentation micro-network for MAV-based tasks
Vijay Kumar, Elijah S. Lee, James F. Keller, Ian D. Miller, Shreyas S. Shivakumar, Ty Nguyen, Giuseppe Loianno, Joseph Harwood, Jennifer M. Wozencraft, Camillo Jose Taylor, Tolga Özaslan, Alex Zhou · AYBU AVESIS · 2019
© 2020 Institute of Electrical and Electronics Engineers Inc.. All rights reserved.Real-time semantic image segmentation on platforms subject to size, weight, and power constraints is a key area of interest for air surveillance and inspection. In this letter, we propose MAVNet: a small, light-weight, deep neural network for real-time semantic segmentation on micro aerial vehicles (MAVs). MAVNet, inspired by ERFNet [E. Romera, J. M. lvarez, L. M. Bergasa, and R. Arroyo, "ErfNet: Efficient residual factorized convnet for real-time semantic segmentation," IEEE Trans. Intell. Transp. Syst., vol. 19, no. 1, pp. 263-272, Jan. 2018.], features 400 times fewer parameters and achieves comparable performancewith somereference models in empirical experiments. Additionally,we provide two novel datasets that represent challenges in semantic segmentation for real-timeMAVtracking and infrastructure inspection tasks and verify MAVNet on these datasets. Our algorithm and datasets are made publicly available.